OpenCL in trading - page 10

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Easy, Effective, Efficient: GPU programming with PyOpenCL and PyCUDA (1)

GPU programming with PyOpenCL and PyCUDA (1)

This video introduces PyOpenCL and PyCUDA, packages for efficient GPU programming with Python. The speaker emphasizes the advantages of OpenCL for its flexibility to talk with other vendor devices, unlike CUDA from Nvidia. The programming model involves indexing information to distinguish between different squares in a grid, allowing for more parallelism and less reliance on memory caches. In addition, PyOpenCL and PyCUDA allow easy communication with and programming of compute devices, thus enabling faster productivity and facilitating asynchronous computing. The speaker also discusses the importance of managing device memory and the availability of atomic operations in PyOpenCL and PyCUDA.

• 00:00:00 In this section, Andreas Faulkner introduces PyOpenCL and PyCUDA as packages for easy, effective, and efficient GPU programming with Python. Faulkner explains that PyOpenCL and PyCUDA enable programming for GPUs via CUDA or OpenCL through a Python interface. Additionally, he highlights the advantages of OpenCL due to its flexibility to talk with other vendor devices in comparison to a CUDA device from Nvidia. Faulkner claims that with GPUs, it is possible to do better than with traditional CPUs by implementing a different system, in which the instructions are controlled by a multitude of coarse, simple components. Ultimately, with PyOpenCL and PyCUDA, programmers can control 16 independent instructions to execute scientific computing workloads.
  
  
• 00:05:00 In this section, the speaker discusses the core ideas of Cindy Acosta, which involve adding more parallelism to solve the problem of memory slowness. By adding more ALUs and increasing the amount of shared storage and context storage, the chip can continue doing useful work even when blocked by memory stalls. The goal is to program an infinite number of cores, as expressing parallelism in a program is much easier than transforming a parallel program into a sequential one. The ultimate hardware design includes 128 independent instruction sets organized in a way that allows for more parallelism and less reliance on memory caches and out of order execution.
  
  
• 00:10:00 In this section, the speaker explains how to map the hardware of a computer into a picture where there are infinitely many forests and failures, with the aim of preserving the true scale nature of hardware. This is achieved by defining work items, with a two-dimensional grid grouping the number of work items. By mapping these groups to a machine, the extra parallelism can be turned into sequential execution. The programming model provided by PyOpenCL and PyCUDA behaves like a pool of parallelism for the chip to drop into, with a transfer to sequential execution only when there is no parallelism left on the chip.
  
  
• 00:15:00 In this section of the video, the speaker explains the programming model of GPU programming with PyOpenCL and PyCUDA. The model involves running a single function multiple times, where each run corresponds to a square in a grid. To distinguish between the different squares in the grid, indexing information is used, such as local and global IDs, and a function is written that uses this information to access data. The speaker goes on to explain that OpenCL is the open computing language used for GPU programming, which provides a runtime code generation and is a flexible way of talking to various compute powers available in the box.
  
  
• 00:20:00 In this section, the speaker discusses the usage and implementation of OpenCL, stating that there are at least three high-quality implementations of it. While CUDA has been around longer and is more visible due to its presence on NVIDIA's webpage, OpenCL has been adopted by several organizations, including Apple. The speaker notes that he has taught a class on OpenCL and found it to be a good idea, with several students opting to use OpenCL instead of CUDA. Additionally, the speaker emphasizes that there is not much conceptually different between OpenCL and CUDA, and the performance differences are often artificial.
  
  
• 00:25:00 In this section, the speaker discusses the architecture of GPU programming, starting from the host and the runtime interface, and describing the different platforms and compute units within them. The speaker then introduces PyOpenCL and its ability to allow Python to communicate with and program the various compute devices, which can boost productivity and allow for spin processing, among other benefits. The use of PyOpenCL is seen as a good fit for utilizing compute devices from a high-level language, such as Python, without having to worry about technical details.
  
  
• 00:30:00 In this section, the speaker discusses the difference between compiling at compile-time versus at runtime and how scripting for GPUs is a defensible thing to do. He explains that for certain code, such as high-level slow-smoke kind of things where speed isn't that important, it makes sense to use a scripting language for GPU programming. Additionally, because the CPU is restricted to being more or less just a traffic cop in GPU programming, using a scripting language like Python can be plenty fast to get things done. The speaker then introduces PyOpenCL and how it allows a user to execute C source code at runtime with native support for compiling.
  
  
• 00:35:00 In this section, the presenter demonstrates GPU programming with PyOpenCL and PyCUDA by starting with an array of random numbers and creating an OpenCL context to create a buffer to transfer the data onto the GPU. They then create a CL program to multiply the data and call it with a command onto a grid of size eight. The presenter emphasizes the simplicity of the program and demonstrates that the program would still run flawlessly with a larger grid size, unlike CUDA. They conclude by confirming that the desired output was obtained and suggest making more changes to the program to help understand the programming model.
  
  
• 00:40:00 In this section, the speaker explains the concept of grid size and workgroup size in PyOpenCL and PyCUDA programming. It is important to note that the global grid size remains the same regardless of the size of the workgroup. Changing the workgroup size can result in a significant difference in performance. The speaker also discusses how to change the program to use a group of 16 by 16 workouts and how to benchmark using one work item per group versus using 256 for category. It is important to keep in mind that the CPU and GPU are communicating with each other and the actual computation runs asynchronously.
  
  
• 00:45:00 In this section, the instructor explains how timings are measured using kernel log and dot wait commands in PyOpenCL. When performing benchmarking, the time measurements are written down before and after the kernel log, and the dot wait command is used at the end to ensure a complete execution of the kernel. The instructor also emphasizes how PyOpenCL and PyCUDA provide complete access to the underlying layers and automatically manage resources, making it easier to increase productivity. Furthermore, these libraries integrate seamlessly with other frameworks and work on all major operating systems, including extensions from vendors such as Nvidia.
  
  
• 00:50:00 In this section, the speaker discusses the availability of atomic operations in PyOpenCL and PyCUDA, stating that they come as part of the base part of the standard and are not emulated if not available in the hardware. The speaker also mentions the use of string representation in code generation, which they say is something that would be built on top of PyOpenCL. The section ends with the speaker emphasizing the importance of carefully managing device memory and referencing the availability of documentation on PyOpenCL and PyCUDA.
  
  
• 00:55:00 In this section, the speaker explains how PyOpenCL and PyCUDA can make programmers more productive and save them valuable time when coding up tasks that would be trivial using open source libraries. They can also generate hype for Python and make it easier for programmers who don't know C++ to write programs quickly. Using multiple contexts in open CL can help to coordinate a program's big computation from a single source.

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Easy, Effective, Efficient: GPU programming with PyOpenCL and PyCUDA (2)

GPU programming with PyOpenCL and PyCUDA (2)

The video discusses various aspects of GPU programming using PyOpenCL and PyCUDA. The speaker explains the importance of understanding the context of the program and highlights the key components of the runtime and device management. They provide valuable insights about command queues, synchronization, profiling, and the buffer in PyOpenCL and PyCUDA. The video also touches on how to execute code in a context via constructing a program from source code and emphasizes the importance of using element-wise operations and synchronization functions in the device. The speaker concludes by discussing the benefits of the staging area and encourages attendees to explore other device-specific operations that are exposed as hooks.

• 00:00:00 In this section, the speaker provides an overview of the PyOpenCL and PyCUDA programming frameworks, discussing the concepts and components of the runtime and device management. The speaker emphasizes the importance of understanding the context of the program, and how to talk to different devices using the OpenCL runtime. The speaker also touches on the implementation details of OpenCL, specifically highlighting the Apple implementation. The speaker concludes with a tour of the "toy store", providing an overview of the different components that make up PyOpenCL and PyCUDA.
  
  
• 00:05:00 In this section, the speaker notes that PyOpenCL and PyCUDA use an ICD loader to find the actual implementation of shared libraries in a directory via dynamic loads. Platforms provide groups of devices that have their own contexts, and once the device is selected, users can create a context by assigning it to the desired device. Context can span multiple devices and can be used for creating programs and commands. The purpose of commands is to mediate between the host and the device and run asynchronously. The speaker explains that work is submitted to a queue, which is sequential by default, and notes that multiple queues can be active on one device, allowing for parallel processing.
  
  
• 00:10:00 In this section, the speaker explains how to set up GPU programming with PyOpenCL and PyCUDA. He discusses creating command queues which are device-specific and can have multiple properties including profiling. He then demonstrates using an Intel processor for vector addition and explains the importance of event identifiers for monitoring time spans of operations. Overall, the speaker emphasizes the usefulness of command queues for GPU programming.
  
  
• 00:15:00 In this section, the speaker explains the importance of synchronization between hosts and events in parallel computing using PyOpenCL and PyCUDA. They discuss how to wait for multiple events at the same time and have members of the command queue wait for each other to ensure safe switching between queues. The speaker also discusses data dependency and how it can be expressed in the implementation to inform the devices how things depend on each other. Additionally, using profiling enables fine-grained timing things and a precise recording of when events occur, giving very detailed performance data.
  
  
• 00:20:00 In this section, the speaker explains how profiling works in GPU programming and how to estimate the time taken for execution. He also discusses the use of markers in the code and how to get the timing data. The speaker introduces directed acyclic graphs and how they can be used when communicating between multiple streams of execution on different GPUs, and the importance of synchronization and dependency management when dealing with memory. Overall, the speaker provides valuable insights into the various aspects of GPU programming using PyOpenCL and PyCUDA.
  
  
• 00:25:00 In this section, the speaker discusses the buffer in PyOpenCL and PyCUDA, which is a chunk of memory without any type information that can be submitted to a kernel sitting on another device. The buffer abstraction provides complete isolation from where the data is stored, ensuring that everything stays efficient if it happens within the same device. The speaker also details three different types of memory allocation: copy, use host pointer, and allocate. The implementation has all the information it needs to know from what device the data needs to go through to be the most efficient. However, the cost of this is that it may be expensive to transfer data from one device to another.
  
  
• 00:30:00 In this section, the speaker explains how to avoid going through the "post" technique of transferring data by associating a buffer with the content and transferring the data into it. However, they note that a consequence of not having a physical location for the buffer is the inability to have pointers to the base that last beyond the life of one kernel. The speaker also mentions that on a cluster, users have a choice to create a context that sends a single view of all the OpenGL devices in the whole cluster on one machine, which can exploit the stiffest vocabulary across the whole front row. To get the memory location, the user attaches a buffer to a contact, and the implementation has no idea which device the memory is active code.
  
  
• 00:35:00 In this section, the speaker explains how to use indices to point to vectors and buffers in PyOpenCL and PyCUDA. Buffers can be specified using memory and the host, and they can be instantiated in a way that satisfies certain requirements. One way of doing this is by locking the transcript to open the memory space for use. The speaker advises that it is usually wise to default to block transfers, as this will ensure that the memory transfer takes place before any data gets reused.
  
  
• 00:40:00 In this section, the speaker discusses how to execute code in a context via constructing a program from source code. The speaker notes that users can include the kernel with certain building and work inside arguments, and oscillate other stuff. Arguments can be a null pointer, numpy size scalars, or anything with a buffer interface. However, it is important to count these arguments properly. The speaker shares that there is a way to avoid explicitly sized integers each time by upfront telling OpenCL about the data types of the scalar sighted and avoid forgetting them. Additionally, the speaker mentions the device manager, which can be used to learn about devices and their memory space qualified in a second. A way to avoid explicitly sized integers each time by upfront telling OpenCL about the data types of the scalar sighted and avoid forgetting them.
  
  
• 00:45:00 In this section, the speaker discusses some confusing and unintuitive choices in PyOpenCL and PyCUDA, such as the naming conventions for memory spaces and the differences between global and local memory. They also highlight the use of memory spaces like device, private, and local memory, as well as textured images and trip IDs. Despite the challenges, the speaker emphasizes the importance of combining these features to write successful algorithms and highlights the usefulness of being able to assign to components.
  
  
• 00:50:00 In this section, the speaker explains the benefits of using element-wise operations such as sine and cosine functions while programming with PyOpenCL and PyCUDA. He further explains that these functions are helpful since you can handle the vector just like a scalar and you can load and store from increment close to a light. He also points out the importance of having synchronization functions in the device like barriers and memory fences, which allow you to synchronize between kernel launches and within the kernel. The memory fences are also important to control the memory operations before you to prevent ordering conflicts.
  
  
• 00:55:00 In this section, the speaker explains the purpose of the staging area for safekeeping of variable reef, where data can be brought between CPU and GPU for uninterruptible and public operation. The speaker also mentions the PyOpenCL, which wraps device-specific operations at the lowest level and makes them available. Additionally, the speaker introduces the "swap" operation, which allows arbitrary and complicated operations that commit with comic. The speaker encourages attendees to ask more questions or explore other device-specific operations that are exposed as hooks.

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Easy, Effective, Efficient: GPU programming with PyOpenCL and PyCUDA (3)

GPU programming with PyOpenCL and PyCUDA (3)

In this section of the video series on GPU programming with PyOpenCL and PyCUDA, the presenter discusses various topics including optimizing code with attributes, memory management, code generation, and the benefits of using PyOpenCL and PyCuda. The presenter emphasizes the advantages of generating multiple varieties of code at runtime and explains how string replacement, building a syntax tree, and utilizing Python and performing languages can help create code that is flexible and efficient. The presenter also warns of potential pitfalls when using control structures in Python, but demonstrates how an abstract approach to analyzing algorithms can help improve parallelism. Overall, the video provides valuable insights and tips for optimizing GPU programming with PyOpenCL and PyCUDA libraries.

The video also discusses strategies for evaluating and choosing from different codes for GPU programming. Profiling is suggested, with analysis of command and event outputs to determine when the code was submitted and the duration of the run. Other evaluation options include analyzing the NVIDIA compiler log and observing the code's runtime. The video also covers a search strategy for finding the best values for a group in PyCUDA and PyOpenCL programming. The speaker recommends using a profiler to analyze program performance and mentions the impact of workarounds for Nvidia profiling patches on code aesthetics.

• 00:00:00 In this section of the video, the presenter reviews the OpenCL spec which he finds well-written and easy to read. Additionally, he reminds viewers to make sure that the garbage collector does not compromise memory when initiating a transfer from a host's memory onto the device. The presenter goes on to explain implicit and explicit workgoups and shows how AutoTune generates different versions of code to enable developers to choose the most appropriate one. Finally, he shows a toy he has created which visualizes the movement of particles on a grid.
  
  
• 00:05:00 In this section, the speaker explains how certain attributes can be used to give extra knowledge to the compiler and improve the performance of the code. He mentions two attributes that can be specified- type and required work group size X Y Z. Type tells the compiler that the main unit of computation in the code is going to be float, for example, and the compiler can make decisions on what register use is going to be like. Required work group size X Y Z can be used to help the compiler carry out multiplication tasks faster and make optimizations on the access pattern. The speaker also mentions page-locked memory, which is memory that's closer to the processor and can be accessed without asking the host for help. It’s hidden behind the command alert post pointer in OpenGL and can be useful in GPU programming.
  
  
• 00:10:00 In this section, the speaker discusses memory and how it's accessible from both GPU and host address space noting how it works for OpenCL and CUDA with some limitations like texturing from linear memory being absent in OpenCL. The speaker also mentions how Apple's implementation of OpenCL is different with features such as a cache that can be problematic for debugging. Additionally, the speaker notes that Intel reportedly doesn't like OpenCL and is pushing its own stuff while Apple has strong-armed them to lament elephants ear. Lastly, the speaker suggests that AMD's GPU implementation is worth checking out, especially for super top-heavy works that require a pile more floating-point power.
  
  
• 00:15:00 In this section, the speaker discusses code generation, which entails creating multiple varieties of code at runtime to adapt code to different situations. Generating code is a useful idea for several reasons, including automated tuning and accommodating a variety of user requests, such as different data types. The speaker suggests that Python is an excellent way to perform text processing and generate code.
  
  
• 00:20:00 In this section, the speaker discusses how to bring flexibility to tight inner loops of code. He explains that when writing libraries, it's important to allow flexibility at a point where the code is in a tight inner loop. He mentions three main ways to achieve this flexibility: string replacement, building a syntax tree, and generating code. The speaker also notes that using a combination of Python and a performing language like PyOpenCL or PyCUDA can help exploit the strengths of each language and create a reasonable way to construct code that is not too slippery. Lastly, he explains the benefits of the NumPy library for linear algebra and how it can help you in addition to runtime cogeneration.
  
  
• 00:25:00 In this section, the speaker discusses the benefits of using PyOpenCL and PyCuda, two Python libraries for GPU programming. These libraries allow for mixing types arbitrarily and can handle vectorization of operations effectively. When working with expression evaluations, these libraries use a facility called an element-wise kernel, which can avoid the need for temporary arrays being created and subsequently discarded. PyOpenCL and PyCuda also offer facilities for data parallel operations, such as element-wise cumulative reduction, which can perform operations like summing things across the holder. The speaker concludes that these libraries make it possible to handle all the different combinations of data types easily while taking care of running operations either in parallel or sequentially.
  
  
• 00:30:00 In this section, the presenter discusses the advantages of leaving a scalar on the GPU instead of transferring it back and forth, which can result in inefficiencies. He also talks about templating engines that can generate web pages and substitute different keywords in a code script. The presenter emphasizes that these techniques are not magic, but simple and useful tools that can greatly benefit programmers.
  
  
• 00:35:00 In this section, the presenter discusses the use of templating engines to simplify the process of generating code by showing examples of how the process works. The templating engine allows for Python expressions to be used between dollar signs, which can help unroll loops and create expansions. The resulting output is source code that must then be manually fed into the CL. The presenter takes questions from the audience as they try to understand the process.
  
  
• 00:40:00 In this section, the speaker discusses the availability of control structures that Python supports but warns that this gives the programmer a lot of rope to hang themselves if they are not careful. They go on to talk about the reduction example and explain how generating code can be done with arbitrary possibilities, as PyOpenCL has a Python feature that allows you to disregard or include all the new nights. They conclude that PI opens the syntax tree, and it is barely justifiable to do this copy and paste method.
  
  
• 00:45:00 In this section, the speaker explains that if a user performs tasks in a well-structured manner by generating code structurally, it could work for building certain parts of a project, but it might not be suitable for a constructing a whole project. The speaker goes on to discuss an example of how to do vector addition and reduction, which is seen as a function to the first two, then another function to the result, and is implemented using a tree-based approach. The user is then asked to decide upon the amount of work they are going to do and wait for, followed by a graphical representation of how it all works.
  
  
• 00:50:00 In this section, the speaker explains how to improve the parallelism in the previous code version to make it more efficient. They suggest using an abstract approach to analyze algorithms based on work and workload to identify how parallel the task is. They mention the company's aim to balance the runtime on worker size and dependencies to improve parallelism. They also give an example of the final version of the reduction code, which includes variables, math expressions, and reduces expressions directly. They then demonstrate code generation to improve performance and double support.
  
  
• 00:55:00 In this section, the speaker discusses the implementation of the reduction expression using PyOpenCL and PyCUDA with examples of how to generate code for a specific number of items. They mention the use of template metaprogramming in PyCUDA and how it can be hard to understand. The speaker argues that PyOpenCL and PyCUDA's ability to generate a variety of code from a single source without redundancy makes it a useful tool.
  
  
• 01:00:00 In this section of the video, the speaker discusses how to evaluate and choose between different codes for GPU programming. They suggest using profiling, which can be turned on using the command Q, and analyzing the command and event outputs to determine when the code was submitted and how long it ran for. Other options for evaluation include analyzing the NVIDIA compiler log, computing the number of bucks provided, and observing the code's runtime. If the number of codes to evaluate exceeds what can be done in one lunch break, they suggest either conducting an exhaustive search or using orthogonal search methods, such as those provided by Mike Rita's compiler cache.
  
  
• 01:05:00 In this section, the speaker discusses a search strategy for finding the best values for a group in PyCUDA and PyOpenCL programming. The strategy involves finding a group, writing down all options, and doing a local target search. The speaker also shares that most of the things people search for are relatively simple, and an expert opinion can be valuable in optimizing code. The speaker recommends using a profiler to analyze program performance and mentions that the code may not be pretty due to workarounds for Nvidia profiling patches.

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Easy, Effective, Efficient: GPU programming with PyOpenCL and PyCUDA (4)

GPU programming with PyOpenCL and PyCUDA (4)

This video series covers various topics related to GPU programming using PyOpenCL and PyCUDA. The speaker shares code examples and discusses the development cycle, context creation, and differences between the two tools. They also touch on collision detection, discontinuous galerkin methods, variational formulations of PDEs, and optimizing matrix-vector multiplication. Additionally, the speaker talks about the challenges of computing matrix products and highlights the performance differences between CPU and GPU in terms of memory bandwidth. The video concludes by emphasizing the importance of performance optimization while using PyOpenCL and PyCUDA.

The video also discusses the advantages of combining scripting and runtime cogeneration with PyOpenCL and PyCUDA. The speaker explains that this approach can improve application performance and make time stepping less challenging. In the demonstration of the Maxwell solution planes and inhale powers, the benefits were evident. The speaker suggests that using these tools in combination is a great idea, and there is potential for further exploration.

• 00:00:00 In this section, the speaker shares his code which is similar to PyOpenCL but in PyCUDA for GPU programming. He allocates the memory for device copies and shows the kernel that performs multiplication of elements. He also mentions how they can have more than one device addresses, and a little about PyCUDA's functionality compared to PyOpenCL. Lastly, he discusses sparse matrix vector calculations and how the conjugate gradient can make a decision of whether it can converge based on the inner process, so the computing can keep going while the data gets transmitted back and forth between the CPU and GPU.
  
  
• 00:05:00 In this section, the speaker discusses the development cycle of using a scripting language as opposed to a compiled code for GPU programming, and the drawbacks of the former. They explain that while a compiled code helps one to catch bugs during compilation and improves performance, a scripting language does not allow for that. However, they contend that the PyCUDA and PyOpenCL packages can help to eliminate this problem by allowing one to invoke the compiler and avoid the wait time between invocations. Additionally, they mention the runtime API and driver API distinction and the requirement of letting the runtime API libraries create the context within which one is working.
  
  
• 00:10:00 In this section, the speaker discusses the differences between PyOpenCL and PyCUDA. The context object in either tool can be created in different ways. However, documentation is available for both of them that makes it easier for users to develop kernels. The speaker encourages the use of micro-benchmarks to model performance and thus optimize performance when writing smart code. They then move on to show how collision detection can be defined in such a way that it works well for a range of linear algebra problems.
  
  
• 00:15:00 In this section, the speaker discusses a model used to specify the principal insufficient to capture distance, but acknowledges that it is not sufficient to capture everything. He then shares code for loading data into shared memory and iterating through the possibility of a kernel. The speaker talks about optimizing for specific solutions and how to potentially reuse a variable within a loop. He then explains the discontinuous galerkin method, which is a finite element method used for time-dependent conservation bonds. The method involves integrating by parts and getting a boundary term across the elements, with a choice to integrate over the boundary of the element.
  
  
• 00:20:00 In this section, the speaker discusses the challenge of how to deal with two different valid values at the interface of the government interface since the test function and the solution space carry discontinuity. The speaker suggests using the theory of Riemann solvers that was developed for a finite volume method. By solving a conservation law principle and selecting either one of the two values along the interface, a weak formanta can be created. This approach gives communication between different values while solving the equation. There are different schemes mathematically that can be used, but using a Riemann solver can solve the equation so it falls into the solution space.
  
  
• 00:25:00 In this section, the speaker discusses the formulation of the variational formulation of PDE, which involves substituting basis functions to introduce element-wise matrices, and the resulting inner product view leads to a mass matrix. They also discuss the inversion of the mass matrix, which can be performed element by element for a fairly simple scheme, and the simplification of the scheme using the EG, which is a good fit for dense data locally and has been typically used as a high-order method.
  
  
• 00:30:00 In this section, the speaker discusses the computational intensity of using higher orders, which makes using PyOpenCL and PyCUDA for GPU programming an attractive option. When dealing with linear conservation laws, certain choices need to be made based on its complexity, and when shooting for medium port, the business becomes more manageable. The asymptotic runtime is dominated by two matrix-vector products per element, and certain computations are more profitable than tensor product elements. The approximation space used is local around the global village space, and exploiting the tensor product structure doesn't provide any advantage.
  
  
• 00:35:00 In this section, the video explores how to optimize matrix-vector multiplication by dividing the workload among different workers. The speaker discusses the tradeoff between using one element per worker or multiple elements per worker, considering factors such as memory usage and coalescing memory access. They also examine the choice between computing at an element-wise granularity or a group granularity, and how to balance data reuse and parallelism. The video concludes by stating that these decisions depend on various factors such as matrix size and output buffer size.
  
  
• 00:40:00 In this section of the video on GPU programming with PyOpenCL and PyCUDA, the speaker discusses granularity in computations and the minimum level of granularity required for a computation to fill a 70, thus satisfying area padding requirements, with multiples of this minimum level of granularity enforced for other computations. The performance aspect and flexibility aspect of the code are discussed, with the speaker presenting a graph showing the sequence of things done in parallel fashion in relation to the size of the box, and emphasizing the lasting value of increasing the performance of the code as opposed to relying on the hardware. A variation formulation and the flux term are also highlighted from a CS perspective.
  
  
• 00:45:00 In this section, the speaker discusses the challenge of transcribing a tight inner loop from a paper written in mathematical notation and implementing it into code. To address this challenge, the implementation should closely match the paper's mathematical notation. Additionally, having a layer of thinking between the executed code and the user's code provides a non-negligible benefit of code generation. The speaker explains that high-performance code can be written using PyOpenCL and PyCUDA, and that the performance is comparable to a hand-tuned implementation on the high end. They also note that they exceed memory bandwidth on a GTX 280, and that using extra cache helps with performance.
  
  
• 00:50:00 In this section, the speaker discusses the challenges of computing matrix products due to limited memory space. Despite computational efficiency, the memory is not enough to store all the work, and researchers have to break down the matrix into smaller bits to perform the operations. They also highlight that the matrix product which works well on short and fat datasets is not easy to tune with GPU systems since no one optimizes it for GPUs. Although CPUs can handle trivial triple loop matrix matrix for short datasets more efficiently, the GPU system is still better - with 16 GPUs performing faster compared to 64 Superior Court PCs.
  
  
• 00:55:00 In this section, the speaker discusses the performance of CPU and GPU in terms of memory bandwidth and the practical comparison of real-world scenarios. He also emphasizes that for practical purposes, it is better to compare actual performance against theoretical peak performance rather than the number of cores added to the machine. The speaker also talks about the potential of improving performance while using double precision and mentions the possibility of manipulating computation to achieve better results without compromising the accuracy of the computation. The section ends with the speaker highlighting some of the key questions related to time integration and actors in GPU programming with PyOpenCL and PyCUDA.
  
  
• 01:00:00 In this section of the video, the speaker talks about the benefits of using scripting and runtime cogeneration together with PyOpenCL and PyCUDA. He explains that it can give multiple benefits, such as making time stepping less painful and improving the performance of applications, as demonstrated with the Maxwell solution planes and inhale powers seen in the video. He concludes by saying that using these tools together can be a great idea and that there is certainly more that can be done.

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Par Lab Boot Camp @ UC Berkeley - GPU, CUDA, OpenCL programming

Par Lab Boot Camp @ UC Berkeley - GPU, CUDA, OpenCL programming

In this video, the speaker provides an overview of GPGPU computation, focusing primarily on CUDA and including OpenCL. The CUDA programming model aims to make GPU hardware more accessible and inherently scalable, allowing for data parallel programming on a range of different processors with varying degrees of floating-point pipelines. The lecture delves into the syntax of writing a CUDA program, the thread hierarchy in the CUDA programming model, the CUDA memory hierarchy, memory consistency and the need to use memory fence instructions in order to enforce ordering of memory operations, and the importance of parallel programming in modern platforms with CPU and GPU. Finally, the speaker discusses OpenCL, a more pragmatic and portable programming model that has been standardized by organizations like Chronos and involves collaboration between various hardware and software vendors, like Apple.

The speaker in the video discusses the differences between CUDA and OpenCL programming languages. He notes that both languages have similarities, but CUDA has a nicer syntax and is more widely adopted due to its mature software stack and industrial adoption. In contrast, OpenCL aims for portability but may not provide performance portability, which could impact its adoption. However, OpenCL is an industry standard that has the backing of multiple companies. Additionally, the speaker talks about the methodology for programming a CPU vs GPU and the use of Jacket, which wraps Matlab and runs it on GPUs. The speaker concludes by discussing how the program changes every year based on participant feedback and encourages attendees to visit the par lab.

• 00:00:00 In this section, the speaker introduces himself and outlines the agenda for the lecture on GPGPU computation, focusing primarily on CUDA and including OpenCL. He gives a brief overview of GPU hardware and its evolution from specialized, non-programmable units for graphics to more powerful and flexible, programmable units with the introduction of CUDA and OpenCL. The CUDA programming model aims to make GPU hardware more accessible and inherently scalable, allowing for data parallel programming on a range of different processors with varying degrees of floating-point pipelines.
  
  
• 00:05:00 In this section, the speaker explains the goal of making SIMD hardware accessible to general-purpose programmers, which requires expressing many independent blocks of computation in a way that allows for scalability. The speaker delves into the syntax of writing a CUDA program, which involves accessing the hardware that the GPU has and using a multiple instruction multiple data thread abstraction that executes in sindi on the actual GPU. The CUDA mem copies are emphasized as the basic way to communicate between the host and the device, and the speaker notes that the communication travels over the PCI Express link in the system, which is relatively slow, making it necessary to minimize data transfers for optimal performance. A brief overview of vector computing is also provided.
  
  
• 00:10:00 In this section, the video explains how to change standard C++ code for vector addition into a parallel CUDA program. By adding tags, the program is compiled to run on the GPU, and threads use block and thread indexes to determine which element of the array each thread should work on. The video also notes that getting simple CUDA programs working is relatively easy, but optimizing for performance takes additional effort. Additionally, the video provides an overview of the CUDA software environment, the hierarchy of threads in the CUDA programming model, and the GPU architecture, which is composed of streaming multiprocessors.
  
  
• 00:15:00 In this section of the video, the speaker discusses the structure of the grids and thread blocks that execute in parallel on the GPU. A grid is a set of up to 32 thread blocks, and each thread block can execute up to 1,000 CUDA threads. Each CUDA thread is a lightweight, independent execution context with its own program state, which can load from any address in the GPU DRAM. Additionally, groups of 32 CUDA threads form a warp, which executes in lockstep and is crucial for high-bandwidth memory access. The speaker explains that warps are a performance optimization detail, but they are important for maximizing the efficiency of the execution hardware.
  
  
• 00:20:00 In this section, the speaker explains the fundamental building blocks of writing code for NVIDIA GPUs using CUDA. A thread block is like a virtualized multi-threaded core that can dynamically configure the number of CUDA threads, registers, and L1 cache it has access to, based on specified data size. A thread block typically includes a data parallel task of moderate granularity, and all the threads within the block share the same block index identifier. The threads within a block can synchronize via barrier-like intrinsic or communicate via fast on-chip shared memory. A grid is a set of thread blocks, and all thread blocks within a grid have the same entry point, differing only in the block index number. The program must be valid for any interleaving of the block execution, and it's a good idea to have many thread blocks per grid to occupy the entire GPU. The highest level of the thread hierarchy is the stream, which is optional but necessary for concurrent execution of multiple kernel functions.
  
  
• 00:25:00 In this section, the speaker discusses the CUDA memory hierarchy, starting with the per-thread local memory that acts as a backing store for the register file. Each CUDA thread has private access to a configurable number of registers specified at compile time, with the memory system aligning to the thread programming model. There is also scratchpad memory that can be used either as 16 kilobytes of L1 cache and 48 kilobytes of software-managed scratchpad or the other way around, dynamically configurable when calling the kernel. The global memory is much more expensive than on-chip memories, with over a hundred times the latency in terms of the number of cycles. The registers and on-chip memories hold the state of the program while the global memory holds the persistent state.
  
  
• 00:30:00 In this section, the speaker discusses the memory hierarchy of GPUs and CPUs. The GPUs have higher bandwidth in aggregate to the L1 caches compared to the global DRAM, with a modder size GPU having approximately 100 gigabytes per second access to DRAM. Additionally, there are other components of the memory hierarchy that are occasionally useful such as the 64-kilobyte constant memory and the CUDA texture memory. Multiple GPUs can be used with each having their own independent global memory that is separate from the CPUs memory. The most important aspect of the CUDA memory hierarchy is the communication within a thread block using the fast onship shared memory, which requires the use of the sync threads function to synchronize the threads within a thread block.
  
  
• 00:35:00 In this section, the lecturer provides a code snippet that transposes a matrix by using shared memory, which is crucial in expressing significantly more concurrency than memory bandwidth. While shared variables can be declared statically via tags on on-begin and end, entire arrays can be allocated and dynamically using extern via integer indices. Scratch pads and synchronous threads are essential for almost all communication within a thread block, with the exception of data that's shared between threads. Accessing shared memory can lead to bank conflicts which can seriously reduce performance. This issue can be mitigated by interleaving pointers so that a bank can be accessed without causing any time lag whatsoever. Finally, the lecturer speaks of atomic memory operations which, although costly, give users the ability to access the same memory location from all threads in a program.
  
  
• 00:40:00 In this section, the speaker discusses memory consistency and the need to use memory fence instructions in order to enforce ordering of memory operations. The hardware automatically synchronizes accesses from multiple threads, but if the programmer doesn't use memory fence instructions, some additions may not take effect. The speaker also explains how certain operations, such as exchange and compare and swap, are useful for implementing spin locks. They caution that memory accesses cannot be assumed to appear globally in the same order they were executed due to how the memory system achieves high performance. Finally, the speaker touches on how CUDA is designed to be functionally forgiving, but the hardware implementation is crucial for getting performance out of it.
  
  
• 00:45:00 In this section, the speaker explains the concept of thread blocks, which are equivalent to a single streaming multiprocessor, and how they have access to several memory resources such as register files, L1 cache, instruction cache, and texture units. A grid, comprising several thread blocks, can take advantage of multiple streaming multiprocessors on a GPU, and frequently, one grid is enough to saturate the entire GPU. However, in scenarios where the grids are not large enough, several streams must execute multiple grids in parallel to cover the entire GPU. To hide the execution latencies of a functional unit and PCI Express transfers, the speaker suggests having multiple warps within the same thread block executing independently while actively using shared memory and L1 cache. Since memory utilization dominates performance tuning, it is essential to reuse every byte loaded from memory at least ten to twenty times to optimize performance, and the speaker provides further guidance on how to improve memory usage.
  
  
• 00:50:00 In this section of the video, the speaker discusses the importance of parallel programming in modern platforms with CPU, GPU, and other processors. He states that every program should take advantage of all the computational resources it needs, and the world is getting more heterogeneous in a lot of ways. He also stresses the need for an industry standard for accessing parallel hardware to write maintainable parallel software, and higher-level programming environments in SDKs for writing parallel code. Additionally, he mentions the various failed programming languages, and that programs must not focus on being beautiful, but rather on finding a good programming model. The speaker also talks about OpenCL, stating that it tries not to be beautiful, and provides an alternative to CUDA.
  
  
• 00:55:00 In this section, the speaker discusses the importance of pragmatism and portability in programming models for GPUs, as they need to be able to run on a variety of hardware and have a long software lifetime. This poses a problem for CUDA, which only runs on Nvidia's hardware, and is very specific and typed, making it difficult for some to adopt. OpenCL, on the other hand, is a more pragmatic and portable programming model that has been standardized by organizations like Chronos and involves collaboration between various hardware and software vendors, like Apple. The high-level view of OpenCL is similar to CUDA in terms of modeling the platform, and it uses command queues, work items, and a similar memory model. However, the syntax for OpenCL is much more complex and has hundreds of different functions for various operations. The vector addition example is presented again with OpenCL code for the kernel function, which involves removing the for loop, adding a kernel tag, and additional tags to pointers.
  
  
• 01:00:00 In this section, the speaker discusses the differences between CUDA and OpenCL, both of which allow users to program different kinds of hardware. While they share similar syntaxes, CUDA offers a more mature software stack and greater industrial adoption, resulting in a wider range of applications. On the other hand, OpenCL aims for portability, but may not provide performance portability, which could hinder its adoption if not addressed. Nevertheless, OpenCL is an industry standard and has the backing of multiple companies, giving developers confidence in their investment in its software. Despite OpenCL being a rival for CUDA, Nvidia still supports it, and the speaker clarifies that Nvidia may not produce optimized code for OpenCL.
  
  
• 01:05:00 In this section, the speaker talks about the similarities and differences between OpenCL and CUDA programming languages. While both have similarities, the CUDA programming language provides a nicer syntax, and it is not necessary to know any of the OpenCL API to use it. The primary reason the compilers are different is entirely pragmatic as NVIDIA chose not to make their OpenCL compiler open source. The methodology for programming a CPU vs. GPU is to target the GPU and get rid of all the parallelization within a thread block, turning a thread block into a P thread or an openmp thread running on a single CPU core, and mapping the warps into SSE instructions. The speaker also talks about Jacket, which wraps Matlab and runs it on GPUs, although it is hard to tell how much percentage-wise a program like Jacket can tap into CUDA's full potential.
  
  
• 01:10:00 In this section, the speaker discusses how they change the program every year based on participant feedback. They plan to send out a form requesting what attendees liked, didn't like, and what could be improved. A panel will be created where speakers will join together to have casual discussions and debates on stage. Attendees have also asked to see the par lab, so they are encouraged to visit and see the space for themselves. Finally, the speaker thanks everyone and wishes them a good rest of their semester.

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Learning at Lambert Labs: What is OpenCL?

What is OpenCL?

In this video about OpenCL, the presenter introduces graphics processing units (GPUs) and their use in graphics programming before explaining how they can be used for general-purpose computing. OpenCL is then presented as an API that allows developers to achieve vendor-specific optimizations while being platform independent, with the speaker highlighting the importance of task design to achieve optimal GPU performance. Synchronization in OpenCL is explained, and a sample GPU program is presented using a C-like language. The speaker also demonstrates how OpenCL can significantly speed up computation and provides advice for working with GPUs.

• 00:00:00 In this section, the presenter explains what graphics processing units (GPUs) are traditionally used for, which is graphics programming such as rendering pictures in real-time or in pre-rendered applications requiring specialized and highly performative hardware. General-purpose GPU programming is discussed as using a graphics card for tasks other than graphics which are highly computationally intensive and require high performance. OpenCL is then introduced as an API that provides a common interface for all vendor-specific frameworks and implementations, making it possible to still get vendor-specific optimizations while being platform independent, which is useful as GPUs are highly specialized and platform-dependent pieces of hardware.
  
  
• 00:05:00 In this section of the video, the speaker discusses the features of tasks that work well for GPU optimization. It is essential to divide tasks into smaller subtasks that can be run simultaneously in different threads. The subtasks should be almost identical in shape and composition to run on multiple threads. The tasks must be independent of each other in terms of synchronization because synchronization across workgroups is not required. The video emphasizes that the more subtasks diverge from each other, the worse the performance becomes, and it may be faster to use the CPU. Therefore, to take advantage of the GPU processing power, tasks must be carefully designed and optimized.
  
  
• 00:10:00 In this section, the speaker explains the main way of synchronization in OpenCL which is the barrier function. This function acts as a checkpoint where all threads have to reach before any of them can proceed. While not super performant, the barrier function is still critical in making sure all threads are synced up at the right moments. The speaker then goes on to present a sample GPU program written in a language that is very similar to C, and explains the different parameters and the logic of the code. Finally, the speaker runs a benchmark test on a program that calculates the first million square numbers using Python and OpenCL.
  
  
• 00:15:00 In this section, the speaker discusses their Python script that takes an array of a million numbers and squares each of them. They then explore the multi-processing library in Python and create a thread pool of size five, but find that running it in parallel actually slows down the computation. Finally, they show an OpenCL example using a C kernel function stored as a string in program memory, and go through the necessary boilerplate code to execute the kernel function. The OpenCL example takes one millisecond to execute, a significant improvement from the previous Python implementations.
  
  
• 00:20:00 In this section, the speaker explains that GPU programming can significantly speed up a bottleneck in code, by reducing the time it takes from 160 milliseconds to around one millisecond, which is a speed up of 100 times. This kind of speed up can make a huge difference and two orders of magnitude can "make or break" a bottleneck in code. The best way for developers to work with GPUs is to have access to a local GPU rather than working on remote machines, although Google Cloud does offer access to GPUs in the cloud. OpenCL is agnostic to different GPU hardware, so it can be used by developers regardless of their GPU hardware. However, developers need to carefully design how they approach problems to get the most out of the GPU, as the subtask function needs to be explicit, so subtasks must be designed carefully.

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Accelerated Machine Learning with OpenCL

Accelerated Machine Learning with OpenCL

In the webinar, "Accelerated Machine Learning with OpenCL," speakers discuss the optimizations that can be made to OpenCL for machine learning applications. One of the speakers outlines how they compared OpenCL and assembly on Intel GPUs using the open-source OneDNN library. They focus on optimizing for Intel hardware but provide interfaces for other hardware and support multiple data types and formats. The group also discusses the challenges of optimizing machine learning workflows with OpenCL and the integration of OpenCL into popular machine learning frameworks. Furthermore, they note that consolidation of OpenCL usage across different frameworks may be overdue. Finally, the speakers discuss the performance benefits of using Qualcomm's ML extension, specifically for certain key operators like convolution, which is important in image processing applications.

In the "Accelerated Machine Learning with OpenCL" video, the panelists talked about the various use cases where machine learning can be employed, including computational photography and natural language processing. They highlighted the need for optimizing machine learning workloads and scaling up based on research results. Additionally, the panelists identified speech as a significant growth area for advanced user interfaces using machine learning. The session concluded by thanking each other and the audience for joining the discussion and reminding participants to provide feedback through the survey.

• 00:00:00 In this section of the webinar, Neil Trevitt, President of the Chronos Group, gives a brief overview of the Chronos Machine Learning Forum, an open forum intended to foster ongoing communication between the Chronos community and the machine learning hardware and software communities. Trevitt notes that OpenCL is already widely used in the machine learning and inference market, and that many recent extensions to OpenCL have relevance to machine learning and inference acceleration. The machine learning forum is an opportunity for developers to provide input and for Chronos to present updates and roadmap information to the wider community.
  
  
• 00:05:00 In this section, the speaker, an AI algorithm engineer at Intel, discusses their work on comparing OpenCL and assembly on Intel GPUs to optimize for machine learning workloads using the open-source library OneDNN. He explains that their team focuses on optimizing for Intel hardware but also provides interfaces for other hardware and supports multiple data types and formats. They use a just-in-time compilation based architecture to pick an optimal implementation based on the problem and hardware, and they optimize for both upcoming and existing integrated GPUs. He goes on to discuss the results of their comparison and the issues they encountered, leading them to their decisions for optimization.
  
  
• 00:10:00 In this section, the speaker discusses how the GPU is divided and how the vector engine and matrix engine perform the main computation. The speaker explains how they optimize for convolutions and data reordering, and how they use subgroups and extensions for the Intel hardware. They mention that there are plans to enable simpler access by adding extensions to sprv and sickle. They also discuss their assembly side of things, using a c plus library for assembly generation on Intel GPUs. Finally, they talk about the significant speedups they were able to achieve on OpenCL through optimization.
  
  
• 00:15:00 In this section, the speaker discusses their analysis of the OpenCL and engine implementations, stating that the OpenCL implementation emitted shorter read instructions and extra instructions under certain conditions. However, they note that these issues are not fundamental and can be resolved by working with Intel's compiler team to modify the implementation. The speaker also discusses the use of assembly, which is useful for revealing gaps in the implementation but is poor for productivity. Finally, they mention their adoption of an assembly generator, which allowed for faster code generation with the ability to specify optimization transforms to the problem.
  
  
• 00:20:00 In this section, the speaker discusses how they can compose their optimizations more effectively using only one specified transform, which can help avoid a proliferation of multiple implementations. Next, the focus shifts to Balaji Kalidas, who discusses the extensions and features that Qualcomm supports to help with accelerated machine learning, which he notes is rapidly growing on mobile devices. While GPUs remain a popular option, the speaker notes that power consumption, low latency dispatch, and synchronization with other blocks on the system-on-chip are all key considerations that need to be addressed to ensure efficient machine learning on mobile devices. The speaker mentions features like zero copy import/export of data and import/export of Android hardware buffer and DMA buff to help with these concerns.
  
  
• 00:25:00 In this section, the speaker discusses the CL qcom ml-ops extension, which is a Qualcomm vendor extension for accelerating machine learning on their GPUs at the op level. The extension uses existing OpenCL constructs as much as possible, including command cues, events, and buffers, and is fully interoperable with other OpenCL kernels. One of the main use cases for the extension is edge training, which allows for transfer learning, personalization, and federated learning, but the primary limiting factor for training at the edge is memory footprint. To address this, the speaker explains the tensor batch one approach, which uses a modified approach to keep the tensor batch size at one and do a number of forward and backward passes until completing the batch. This approach allows for the same results as training with a larger batch size while reducing memory footprint.
  
  
• 00:30:00 In this section, the speaker discusses several OpenCL extensions that can accelerate machine learning tasks. The first extension mentioned is an eight-bit dot product vendor extension that can give significant performance benefits when implementing eight-bit quantized DNNS. The next extension discussed is the "clq com recordable queues," which allows for the recording of a sequence of ND range kernel commands that can be replayed with a special dispatch call, giving significant improvements in CPU power consumption and dispatch latency, which is crucial in streaming mode machine learning use cases. Other extensions like zero copy, subgroup operations, floating-point atomics, generalized image from buffer, and command buffer recording and replay are also useful for machine learning and are available as Qualcomm extensions or are shipping from Chronos.
  
  
• 00:35:00 be more efficient to have a larger batch of kernels that can be submitted all at once, rather than submitting them individually. This is where recordable queues come in, as they allow for recording and pre-processing of a large batch of kernels with only one or two arguments changing between each instance. This significantly reduces the amount of work that the implementation has to do and saves CPU power. Additionally, it helps to ensure maximum GPU utilization and minimize idle periods between dispatches. This is especially important for machine learning models that require hundreds of kernels to be run in sequence. Overall, recordable queues are a valuable extension for improving the efficiency of machine learning acceleration using OpenCL.
  
  
• 00:40:00 In this section, the group discusses the challenges of optimizing machine learning workflows with OpenCL, including determining the optimal time and size of batching work, as well as flushing. They mention that tools like Carbon Queues can help solve these problems. The issue of compile times being a major obstacle with OpenCL is also discussed, but it's not a simple problem to solve. The group suggests the use of specialization constant at the OpenCL level to potentially reduce the number of kernels generated, but the implementation needs a lot of work. They also discuss the potential use of LLVM for performance optimization but point out that it currently has slow compile times as a major issue.
  
  
• 00:45:00 In this section of the transcript, the speakers discuss the challenges of compiling machine learning applications at runtime and the use of pre-compiled binaries. They also touch on the potential solutions provided by MLIR, a multi-level solution, and how it compares to the acceleration at the graph level. The speakers agree that the vendor provided extension could be used for a few key meta commands while a graph compiler or writing your own kernels could be used for everything else, giving the best of both worlds.
  
  
• 00:50:00 In this section of the video, the speakers discuss the integration of OpenCL into popular machine learning frameworks, specifically on mobile devices. They mention that there are already a number of open source frameworks that use OpenCL, and that TensorFlow Lite already has an OpenCL backend that runs well. However, they note that performance and performance portability remain a challenge when integrating OpenCL into generic frameworks, as different vendors may need to contribute to maintain performance with a generic implementation. They also suggest that consolidation of OpenCL usage across different frameworks may be overdue.
  
  
• 00:55:00 In this section, the speaker explains that there is a significant performance benefit from using Qualcomm's ML extension compared to just using TVM or Tensorflow Lite. The benefit will differ depending on how much effort the developer puts into writing their own kernel and optimizing it for GPUs. There is also a clear advantage for certain key operators, such as convolution. The speaker expects to offer further value by accelerating these key operators in the future. The panel also discusses the application domains that are driving the demand for machine learning acceleration, with image processing being a dominant area.
  
  
• 01:00:00 In this section of the video, the panelists discussed the use case areas for machine learning, such as computational photography and natural language processing. They also talked about the challenges in optimizing machine learning workloads and the need to scale up based on research results. Furthermore, the panelists pointed out that advanced user interfaces using machine learning will be a significant growth area, and speech is a part of this. Finally, the session ended, and the panelists thanked each other and the audience for joining the discussion, and the moderator reminded participants to fill out a survey for feedback.

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Mandelbulber v2 OpenCL "fast engine" 4K test

Mandelbulber v2 OpenCL "fast engine" 4K test

This the trial of rendering flight animation using Mandelbulber v2 with partially implemented OpenCL rendering engine. There reason of this test was to check stability of application during long rendering and how rendering behaves when camera is very close to the surface. Because OpenCL kernel code runs using only single precision floating point numbers, it's not possible to do deep zooms of 3D fractals. To render this animation in 4K resolutiuon it took only 9 hours on nVidia GTX 1050.

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Mandelbox flight OpenCL

Mandelbox flight OpenCL

This is a testrender of the mandelbox fractal rendered with Mandelbulber v2 OpenCL alpha version.

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[3D FRACTAL] Prophecy (4K)

[3D FRACTAL] Prophecy (4K)

Rendered in 4K from Mandelbulb3D.