OpenCL in trading - page 4

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AMD Developer Central: OpenCL Technical Overview. Episode 2: What is OpenCL™? (continued)

AMD Developer Central: OpenCL Technical Overview. Episode 2: What is OpenCL™? (continued)

In this video, Justin Hensley discusses the platform and memory models of OpenCL, which are important to understand when using OpenCL to accelerate applications. He explains that a host is connected to one or more OpenCL devices, such as GPUs or multi-core processors, which have compute units that execute code in a single instruction multiple data model. Work items have private memory, while work groups have shared local memory, each device has global and constant memory, and developers must explicitly manage memory synchronization and data to obtain maximum performance. In addition, Hensley discusses OpenCL objects such as devices, contexts, queues, buffers, images, programs, kernels, and events, which are used to submit work to devices, synchronize and profile data. Finally, he outlines how to execute an OpenCL program in three easy steps: creating program and kernel objects, creating memory objects, and creating command queues with events to ensure proper kernel execution order.

• 00:00:00 In this section, Justin Hensley explains the platform and memory models of OpenCL, which is important to understand when using OpenCL to accelerate applications. A host is connected to one or more OpenCL devices (GPU, DSP or multi-core processor), which have compute units that execute code in a single instruction multiple data model. In terms of memory, the host processor has memory that is accessible only by the CPU, while the compute device has global and constant memory (not synchronized) and each work item has its own private memory that only it can access. Work groups have a shared local memory and developers must explicitly manage memory synchronization and data if they want to obtain maximum performance from their devices. Finally, Hensley discusses OpenCL objects, such as devices, contexts, queues, buffers, images, programs, kernels and events, which are used to submit work to devices, synchronize and profile data.
  
  
• 00:05:00 In this section, the speaker explains how to execute an OpenCL program in three easy steps. First, you create program objects to build the source code and kernel objects containing the code to be run on various devices with arguments. Second, you create memory objects, either images or buffers. Third, you create command queues and use them to enqueue work for different devices, but the work order may be in or out of order. Hence, events are used to ensure that kernels are executed in the order needed when dependencies exist.

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AMD Developer Central: OpenCL Technical Overview. Episode 3: Resource Setup

AMD Developer Central: OpenCL Technical Overview. Episode 3: Resource Setup

In Episode 3 of the OpenCL tutorial series, the speaker delves into resource setup and management in OpenCL, covering topics such as memory objects, context, devices, and command queues. The process of accessing and allocating memory for images is also discussed, with a focus on the read and write image calls and the supported formats. The characteristics of synchronous and asynchronous memory operations are examined, with an explanation of how the OpenCL event management system can be used to guarantee data transfer completion. Finally, users are advised to query device information with the CL get device info call to choose the best device for their algorithm.

• 00:00:00 In this section, AMD's Justin Hensley discusses resource allocation in OpenCL, focusing specifically on memory objects and context and devices setup. He explains how to query the system to find available devices, create a shared context, and set up command queues to talk to the devices. Hensley also notes that multiple cores of a CPU are considered one OpenCL device, and that devices in different contexts cannot share data. To choose the best device for an algorithm, users can query the OpenCL runtime for device information with the CL get device info call to determine the number of compute units, clock frequency, memory size, and supported extensions. Finally, Hensley describes buffers as simple chunks of memory and images as opaque 2D and 3D formatted data structures.
  
  
• 00:05:00 In this section, the video explains how OpenCL processes images and how accessing the images through the read and write image calls is necessary. The format and sampler of data for an image is also discussed and how the CL get supported image formats call should be used to determine the supported formats. To allocate an image buffer, the format and size are set, and the CL create buffer call is used to create a buffer object for input and output data. CL in queue read and CL in queue right buffer are commands used to read and write data from and to memory objects, respectively. If a memory region needs to be mapped
  to the host address space, the CL in queue map buffer is used. Lastly, the CLN queue copy buffer is used to copy memory between two memory objects.
  
  
• 00:10:00 In this section, the speaker explains that data can only be shared with memory objects that are allocated within the same context, and all operations can be done synchronously or asynchronously. Synchronous operations are performed when the blocking call is set to CL true, meaning that data transfer will block until the actual memory operation takes place. This can take a while depending on the memory's location. Alternatively, with CL false, it'll be an asynchronous call, and one has to use the open CL event management system to guarantee that the memory is completely copied before it's used.

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AMD Developer Central: OpenCL Technical Overview. Episode 4: Kernel Execution

AMD Developer Central: OpenCL Technical Overview. Episode 4: Kernel Execution

In this section, Justin Hensley covers the topic of kernel execution in OpenCL, explaining that kernel objects contain a specific kernel function and are declared with the kernel qualifier. He breaks down the steps for executing a kernel, including setting kernel arguments and enqueuing the kernel. Hensley emphasizes the importance of using events to manage multiple kernels and prevent synchronization issues, and he suggests using CL wait for events to wait for them to be completed before proceeding. The video also goes into detail about profiling the application to optimize kernels that take the most time to execute.

• 00:00:00 In this section, Justin Hensley discusses kernel execution in OpenCL. He explains that kernel objects encapsulate a specific kernel function in a program and are declared with the kernel qualifier. The program object encapsulates the program source or precompiled binary from disk and a list of devices. Once the program object is built, the user can compile it for the devices they have at runtime. After the kernel has been built, it needs to be executed with the two basic steps for execution being to set the kernel arguments and enqueue the kernel. To set the arguments, the user would use the function called "CL Set Kernel Arg" with the first argument being the kernel that is needed to be executed.
  
  
• 00:05:00 In this section, the video explains how to set the size of arguments and actually execute the kernel. The example used is processing images with a global size set to an image with an image height of a given size. The video explains that OpenCL runtime in queues tasks asynchronously, so it is up to the programmer to use events to track the execution status. The video also explains different ways to synchronize commands and explicitly synchronize between queues using events. The video provides examples of one device and one queue and two devices and two queues, explaining the importance of using events to avoid explicit dependency issues.
  
  
• 00:10:00 In this section, Justin Hensley discusses how to manage kernels and events when using OpenCL. He explains that using events is important when managing multiple kernels and preventing synchronization issues. He suggests using CL wait for events, which waits for all events to be completed before continuing, and in queue wait for events, which in queues a block point for later use by the OpenCL runtime, allowing the application to continue running without blocking. Additionally, CL get event profiling info can be used to profile the application so that developers can optimize kernels that are taking the most time to execute.

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AMD Developer Central: OpenCL Technical Overview. Episode 5: Programming with OpenCL™ C

AMD Developer Central: OpenCL Technical Overview. Episode 5: Programming with OpenCL™ C

This video discusses various features of OpenCL™ C language including work item functions, workgroup functions, vector types, and built-in synchronization functions. The video emphasizes the importance of using correct address space qualifiers for efficient parallel code writing and sharing of memory between work groups. The concept of vector types is discussed in detail along with the use of the correct memory space for kernel pointer arguments, local variables, and program global variables. Additionally, built-in math functions and workgroup functions such as barriers and memfences are covered with a suggestion to check these functions at runtime.

• 00:00:00 In this section, AMD's Dustin Hensley talks about OpenCL™ C language features, which include work item functions, workgroup functions, vector types, and built-in synchronization functions. OpenCL is based on ISO C99, with the restrictions of no standard C99 headers, function pointers, recursion, variable-length arrays, and bit fields. With OpenCL, there are address space qualifiers to allow for efficient parallel code writing, while also enabling sharing of memory between work groups. Additionally, there's optimized access to images through built-in image functions, and built-in runtime functions to access runtime information. Hensley demonstrated a simple data parallel kernel that uses work item functions and showed how different OpenCL functions and variables can be used to build that kernel.
  
  
• 00:05:00 In this section, the concept of vector types in OpenCL is discussed. These vectors types are designed to be portable across different runtimes and have certain characteristics that make them Indian safe, aligned at vector length, and have built-in functions. The video then shows several examples of vector operations such as creating a vector literal and selecting specific components of the vector. Additionally, it is noted that there are several different address spaces in OpenCL and it is important to use the correct one for kernel pointer arguments, local variables, and program global variables. Failure to specify a memory space can lead to defaulting to private and causing issues.
  
  
• 00:10:00 In this section, it is explained that converting data from global, local or private memory spaces to another memory space using pointers is not allowed in OpenCL. Copying data explicitly is necessary for the required memory spaces. Regarding the semantics and conversions, C99 rules are followed for scalar and pointer conversions, and no implicit conversions are allowed for vector types. The importance of being explicit is highlighted by using specific functions to determine the kind of rounding that is carrying out in the operation, rather than relying on the machine to handle it. Built-in math functions of the OpenCL such as log function offer different flavors like full precision, half precision and native function to handle ambiguous C99 library edge cases more efficiently, and different methods are available to go between different data types using this underscore type or convert one type to another type.
  
  
• 00:15:00 In this excerpt, the speaker discusses the built-in workgroup functions and extensions of OpenCL C. These functions include synchronization tools such as barriers and Memphis's that allow for the synchronization of memory. The speaker also talks about the importance of using all work items in a workgroup to execute the same function rather than setting up a barrier that not all items will hit. Additionally, the speaker talks about the various extensions, including the atomic functions and selecting rounding modes at compile time. The speaker recommends going to the specification to read more about these functions and extensions and checking them at runtime.

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How to use OpenCL for GPU work

How to use OpenCL for GPU work

The video introduces OpenCL as an open standard tool that can work on most new graphics cards in Windows with required installation of either CUDA or specific graphics drivers depending on the card. The speaker describes a simple program, the process of creating a kernel, buffers for data, setting kernel arguments and global work size, and running the workload on the device in OpenCL, comparing it to CUDA. The parameters involved in creating a kernel in OpenCL for GPU work, enqueue read buffer, de-allocating memory were explained with sample codes to check calculations. By showcasing a little program that applies subtle blur to grayscale images using OpenCL, the presenter highlights that OpenCL has more boilerplate code than CUDA but is an open and standard solution applicable to different graphics cards and can be reused on different systems regardless of the manufacturer.

• 00:00:00 In this section, the speaker introduces OpenCL and discusses how it is an open standard that can work with most newer graphics cards in Windows, with the requirement of installing either CUDA or the specific graphics driver depending on the card. The speaker then provides a simple program and describes how it works in OpenCL, comparing it to CUDA. They go through the process of creating a kernel, creating buffers for data, and setting kernel arguments and global work size before running the workload on the device.
  
  
• 00:05:00 In this section, the speaker explains the parameters involved in creating a kernel in OpenCL for GPU work. The global work size parameter is the number of units you want to work with, while the local work size is how much you want to do in each unit. The global work size needs to be some multiple of the local work size, and although you can run work without specifying the global local work size, it's better to set both so that you know what dimensions you're working on. The speaker then goes on to explain the enqueue read buffer, how to deallocate memory, and provides a sample code to check that all calculations were done correctly. Finally, the speaker compares their example with a workload that does a blur on an image, showing the parameters and the use of tuples.
  
  
• 00:10:00 In this section, the speaker explains code changes to a previous example and presents a kernel that will perform a subtle blur on an image. After creating pointers and buffers of different sizes, the speaker sets arguments to the kernel before fetching memory back and freeing pointers. Finally, the speaker reads grayscale images, sets pixels back to the result, and writes the grayscale image out.
  
  
• 00:15:00 In this section, the presenter showcases a little program that applies a subtle blur to a grayscale image using OpenCL. The presenter notes that OpenCL has more boilerplate code compared to CUDA, recommending the use of a class or object to keep all kernel program and command queue variables organized. However, the presenter highlights that OpenCL is an open and standard solution that works across different graphics cards and can be reused on different systems without tying oneself to a specific brand or manufacturer. Overall, the presenter provides a useful introduction to using OpenCL for GPU work.

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EECE.6540 Heterogeneous Computing (University of Massachusetts Lowell)

1. Brief Introduction to Parallel Processing with Examples

This video provides a brief introduction to parallel processing with examples. The speaker explains that parallel computing involves breaking a larger task into smaller subtasks to be executed in parallel. Two main strategies for achieving this are divide and conquer and scatter and gather. The video provides examples of natural and man-made applications that inherently have a lot of parallelism, such as human senses, self-driving cars, and cell growth. The video also discusses the benefits of parallel processing and demonstrates how it can be applied to sorting, vector multiplication, image processing, and finding the number of occurrences of a string of characters in a body of text. Finally, the video introduces the reduction process, also known as the summation process, for collecting and processing the results obtained from parallel resources.

• 00:00:00 In this section, the speaker introduces the concept of parallel computing and explains that it involves breaking a larger task into smaller subtasks to be executed in parallel. Two main strategies for achieving this are divide and conquer and scatter and gather. The speaker gives examples of natural and man-made applications that inherently have a lot of parallelism, such as human senses, self-driving cars, and cell growth. Additionally, the speaker provides an example of a sorting problem and explains how it can be approached using the divide and conquer strategy.
  
  
• 00:05:00 In this section, the speaker discusses two examples of parallel processing, starting with sorting using merge sort as an example. The long unsorted lists of integers are broken down into smaller sub-problems of two integers per group, and four computing units map and compare the sub-problems to achieve the final sorted sequence of integers. The second example discussed is vector multiplication, which is inherently data parallel as each multiplication operation is independent of the others. This problem has low arithmetic intensity, making it simple and fast to perform. The speaker also briefly mentions the concept of arithmetic intensity to highlight the trade-off between computation and memory access in different types of processing problems.
  
  
• 00:10:00 In this section, the speaker discusses the benefits of parallel processing and how it allows for the efficient utilization of computing resources. He explains that with parallel processing, you can perform multiple computations at the same time, without the need to load and unload data constantly. The concept of task parallelism is introduced, where multiple computing units work independently on different data. The example of image processing is used to illustrate how data can be passed through a pipeline of multiple functional units to maximize computation. By implementing parallel processing, computing units can work simultaneously, reducing wait times and increasing the speed of computation.
  
  
• 00:15:00 In this section, the concept of parallel processing is further explained using an example of finding the number of occurrences of a string of characters in a body of text. The problem can be divided into a comparison of potential matches that can be done through individual word comparison, which can be parallelized. The comparison can also be done even more granularly by comparing each letter in parallel. The data set is divided into smaller units to perform the same operation in parallel, making the task of comparison highly parallel.
  
  
• 00:20:00 In this section, we learn about the second stage of parallel processing, which is the reduction process, also known as the summation process. Here, the collection phase collects the comparison results from individual competitors and gathers them for post-processing. The final result is produced by collecting intermediate results from parallel resources and adding them up. If the output from the comparison units show a match, the final number obtained after adding all these outputs indicate how many times a word occurred in the original text.

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2. Concurrency, Parallelism, Data and Task Decompositions

2. Concurrency, Parallelism, Data and Task Decompositions

The video delves into the concepts of concurrency and parallelism, as well as the usage of task and data decompositions, and the techniques for data decomposition for parallelism and concurrency. Amdahl's Law is explored as a means of calculating theoretical speedup when running tasks on multiple processors. The importance of task dependency graphs is highlighted in identifying the inter-task dependencies when breaking down a problem into subtasks. Methods for data decomposition, such as input data and row vector partitioning, are indicated as useful for carrying out computation. Atomic operations and synchronization are described as vital to generate the correct result after all sub-tasks are complete.

• 00:00:00 In this section, the video introduces the concepts of concurrency and parallelism. Concurrency occurs when two or more activities are happening at the same time, which may be on different processors or even a single processor employing time-sharing techniques. Parallelism, on the other hand, strictly means that two activities are executed simultaneously on different hardware execution units, such as CPUs or FPGAs. The video also discusses Amdahl's Law, which is used to calculate the theoretical speedup when running tasks on multiple processors. Despite some tasks having to remain in serial, tasks that can be redesigned to run in parallel can be carried out using processing units, such as GPUs, FPGAs or multicore processors.
  
  
• 00:05:00 In this section, the speaker discusses the concept of parallel computing and how it has been implemented in CPU architectures, particularly in Intel's Pentium processors. They explain that in traditional processor architecture, instruction-level parallelism is often utilized to execute independent instructions simultaneously, resulting in improved performance. However, in their class, they focus on task and data parallelism and how these higher-level parallelisms can be exploited using algorithms and software threads. They introduce the concept of task decomposition, which involves breaking an algorithm into individual tasks, and data decomposition, which involves dividing a dataset into discrete chunks that can be operated on in parallel.
  
  
• 00:10:00 In this section, the video discusses the concept of task dependency graphs and how they are useful in describing relationships between tasks when decomposing a problem into subtasks. If tasks do not have dependencies, they can be executed in parallel, which allows for more efficient processing. The video also introduces the concept of data decomposition, which involves dividing the data into different tasks for computation. The examples of image convolution and matrix multiplication demonstrate how the output data can be used to determine how the data can be decomposed into different groups or partitions.
  
  
• 00:15:00 In this section, the speaker discusses different techniques for data decomposition for parallelism and concurrency. The first technique involves partitioning data into row vectors in the original matrix for one-to-one or many-to-one mapping. The second technique involves input data decomposition, which has one input data that corresponds to multiple output data. Examples of this include tar gram histograms and searching for sub-strings. To compute the final result from these intermediate data parts, synchronization and atomic operations may be necessary to ensure that all sub-tasks are complete and the correct result is generated.

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3. Parallel Computing: Software and Hardware

3. Parallel Computing: Software and Hardware

The video discusses different approaches to achieving high levels of parallelism in computing. The speaker describes the hardware and software techniques used to perform parallel computing, including instruction level parallelism (ILP), software threads, multi-core CPUs, SIMD, and SPMD processors. The video also explains the importance of parallelism density and the concept of computing/processing units, which allow for efficient parallel computing. Additionally, the speaker discusses the challenges of creating atomic operations for synchronization purposes and the need to restructure problems for efficient execution on GPUs.

• 00:00:00 In this section, the speaker discusses different approaches to achieving high levels of parallelism. During the early days of processor design, people relied on instruction level parallelism (ILP) and software threads to achieve parallelism. However, these designs are not done automatically, and the programmer needs to be experienced in designing these software applications. In terms of hardware, there are different types of processors available for parallel computing tasks. Multi-core CPUs are designed for task parallelism, while SIMD processors are designed to take advantage of data parallelism. GPUs are best for data parallelism tasks because they can perform multiple data at the same time on hundreds or even thousands of cores.
  
  
• 00:05:00 In this section, the speaker discusses the concepts of SIMD and SPMD, which are commonly used in parallel computing. SIMD stands for Single Instruction Multiple Data, in which each core can perform operations on different data at the same time. On the other hand, SPMD stands for Single Program Multiple Data, in which multiple instances of the same program work independently on different portions of data. Loop strip mining is a popular technique to split data parallel tasks between independent processors, which can utilize the vector unit to execute iterations at the same time. The speaker provides an example of vector addition using SPMD with loop trip mining, where each program runs on different parts of data.
  
  
• 00:10:00 In this section, the speaker explains how different processors can work on different parts of the data in parallel computing, using the example of executing each chunk of data as an independent thread. The cost of creating threads for GPUs is high, so the computation expected on each processor should be bigger, known as parallelism density. For FPGAs, the overhead of creating threads is very low, so there can be a large number of SGMD execution instances. Single instruction multiple data (SIMD) allows for the execution of one instruction on multiple data simultaneously, with many arithmetic logic units (ALUs) executing the instruction together. Parallel algorithms can reduce the amount of control flow and other hardware in favor of the ALU units.
  
  
• 00:15:00 In this section, the speaker explains the concept of computing/processing units that are used within a chip for computation. They can take data inputs and perform operations simultaneously, allowing for efficient parallel computing. The architecture is based on SMID (Single Instruction Multiple Data) and is widely used in GPU hardware. The speaker highlights the importance of atomic operations for synchronization purposes, but warns about the large overhead cost of these operations. Problems that are decomposed using input data partitioning will likely need to be restructured for efficient execution on a GPU.

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4. Two Important Papers about Heterogeneous Processors

4. Two Important Papers about Heterogeneous Processors

The video covers various papers related to heterogeneous computing, including trends in processor design and energy efficiency, the benefits of using customized hardware and specialized accelerators, the importance of balancing big and small cores, and the challenges of data movement and efficient communication between cores. The papers also discuss the need for understanding scheduling and workload partition when working with heterogeneous processors and the use of programming languages and frameworks like OpenCL, CUDA, and OpenMP. Overall, the papers highlight the potential benefits of utilizing multiple cores and accelerators to maximize performance and energy efficiency in heterogeneous computing environments.

• 00:00:00 In this section, the speaker discusses two important papers related to heterogeneous computing which were published in the past decade. The first paper talks about the trend of going from a single processor core to multiple cores and the efficient utilization of unship transistors to gain more performance. The second paper is a survey paper that talks about how computer architects, programmers, and researchers are moving towards a more cooperative approach in using CPU and GPU together to maximize both performance and utilization. The speaker also talks about the trend of going from single-core performance towards molecule performance or throughput-wise performance by using heterogeneous cores and accelerators. The transcript also includes a graph showing the increase in transistor counts on a single chip from 1971 to 2009 and highlights the major benefits of using heterogeneous computing.
  
  
• 00:05:00 In this section, the video discusses transistor scaling and how it can allow for more transistors on a single chip, leading to better performance and energy efficiency. The video then presents a diagram that shows the different techniques used to design microprocessors, such as adding more cores or implementing speculative execution. Although there is not a significant increase in performance each year, the energy efficiency has improved by almost five times, allowing for more tasks to be performed at a higher throughput. The video also introduces Pollock's rule, which is a simple observation about the performance of processors.
  
  
• 00:10:00 In this section, the speaker discusses the performance increase in processors as the area or number of transistors increases. The relationship between these factors is approximately the square root of the number of transistors. On the other hand, DRAM density increases nearly double every two years, but performance in terms of read/write speed is not keeping up. The total energy budget is flat, so even reducing transistor size and increasing the number of transistors would not significantly improve performance. Leakage power becomes predominant as the transistor size decreases, which means that relying on frequency increase, reduction in supply voltage, and capacitance of connections and wires alone cannot achieve the intended performance goals, such as terabit or more operations.
  
  
• 00:15:00 In this section, the speaker discusses the design of multiple cores in heterogeneous processors, highlighting that it may not be beneficial to have large, uniform cores. Instead, customizing hardware with fixed or programmable accelerators and combining logic with the cores can lead to better utilization of the cores. The speaker presents an example where a design with 30 smaller cores (5 million transistors each) outperforms a design with six larger cores (25 million transistors each) while using the same total number of transistors (150 million). The trade-off between throughput and single-core performance can also be optimized by maintaining a balance between big and small cores.
  
  
• 00:20:00 In this section, the speaker discusses the use of heterogeneous processors for customized hardware and the benefits of using smaller, customized cores instead of general-purpose CPU cores. By utilizing specialized logic to build functional units like multipliers or FFT units, designers can achieve higher energy efficiency than with general-purpose designs. Additionally, the paper talks about the challenges of data movement and the importance of having efficient memory hierarchies and interconnects for efficient communication between cores. The paper proposes a split of 10% for specialized accelerators and cores, rather than the traditional 90% for building superscalar out-of-order processors to achieve better single thread performance.
  
  
• 00:25:00 In this section, the video discusses two papers about heterogeneous processors. The first paper talks about the challenges faced in data movement and energy efficiency. One design trend is voltage scaling allowing for running different cores at different speeds, which can greatly reduce energy consumption depending on workload and task scheduling. The second paper discusses the trend towards large-scale parallelism and heterogeneous cores with varying sizes and flexible frequency and voltage. Additionally, there is a move towards using different hardware accelerators built onto the chip and focusing on efficient data positioning to reduce unnecessary data movement. The paper acknowledges the unique strengths of different CPU architectures and designing algorithms to match future CPU features.
  
  
• 00:30:00 In this section, the speaker discusses the importance of understanding scheduling techniques and workload partition when working with heterogeneous processors such as CPU FPGA. Scheduling involves deciding when a sub-task should run on a specific processor, while workload partition deals with data and task partitioning. The speaker also mentions various programming languages and frameworks like OpenCL, CUDA, and OpenMP for different types of processors.

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5. Overview of Computing Hardware

5. Overview of Computing Hardware

The video provides an overview of computing hardware, discussing topics such as processor architectures, design considerations, multi-threading, caching, memory hierarchy, and the design of control logic. It also explains how a program is a set of instructions that a computer follows to perform a task and the different types of programs, including system software and applications. The video emphasizes the importance of the hardware components of a computer, such as the CPU and memory, which work together to execute programs and perform tasks.

• 00:00:00 In this section, the speaker introduces the topics that will be covered in the class, which includes processor architectures and innovations, architectural design space, CPU and GPU architecture, and FPGA as a high-performance emerging architecture. The speaker also talks about the origins of OpenCL and how it was gradually shaped by different vendors based on the processors they provide, leading to a relaxed consistency block-based paradigm model that achieves platform independence. The performance of OpenCL programs still depends on the implementation, algorithm, and how well it can be mapped to the hardware architecture. The design of different processors requires many considerations and trade-offs such as whether to have a single-core processor or multiple processors.
  
  
• 00:05:00 In this section, the video explains some of the design considerations when it comes to computing hardware, such as superscalar cores and instruction scheduling. The video also discusses multi-threading as a way to increase the amount of useful work a processor can handle, as well as the importance of caching and memory hierarchy. Heterogeneity in processors is becoming more common, which includes CPUs with GPUs, FPGAs, and big and small cores. Lastly, the design of control logic is crucial, enabling the reordering of instructions to exploit instruction level parallelism in complex control flows.
  
  
• 00:10:00 In this section, the video explains that a program is a set of instructions that a computer follows in order to carry out a specific task. The program is made up of code written in a programming language, which is then compiled or interpreted into machine language that the computer can understand. The video goes on to describe the different types of programs, including system software like operating systems and device drivers, as well as applications such as word processors and games. The hardware components of the computer, such as the CPU and memory, work together to execute these programs and perform the desired tasks.