OpenCL in trading - page 5

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6. Superscalar and VLIW

6. Superscalar and VLIW

The video explores how processors use superscalar execution to detect and extract parallelism between binary instructions to enhance performance. It discusses the importance of control logic in identifying instances where instructions can run simultaneously, such as with a lack of dependencies between them. The video also introduces two examples of processor design, superscalar and VLIW, with the latter shifting the responsibility for detecting dependencies to compilers, generating long instruction words to be executed in parallel. While VLIW reduces runtime checking, unused spots in the long instruction word can still cause wastage in the execution unit.

• 00:00:00 In this section, the video explains how processors utilize superscalar execution to improve overall performance in programs with binary instructions. Programmers rely on well-designed processors to execute their programs correctly, and so the responsibility falls upon the processor to identify and extract parallelism from the instructions to increase performance. The video provides an example of a dependency graph between instructions, and while there are dependencies between certain instructions, processors can execute others simultaneously due to a lack of dependencies between them. It is the job of the control logic within the processor to identify these instances and execute instructions in the most efficient way possible to maximize performance.
  
  
• 00:05:00 In this section, the video discusses two examples of architectural designs in processors - superscalar and VLIW. The first example shows how the processor can detect dependencies between instructions and schedule them accordingly to save time. The video also highlights the possibility of speculative execution for branch instructions. The second example discusses the VLIW design, which shifts the responsibility of detecting dependencies to compilers. The compiler generates long instruction words comprising multiple instructions that can be executed in parallel, making the processor design simpler.
  
  
• 00:10:00 In this section, the concept of a very long instruction word (VLIW) is explained. This allows for multiple instructions to be packed into one long instruction word, which is fetched and decoded together. The VLIW shifts the responsibility to compilers to discover opportunities for executing instructions at the same time before the program is run, and reduces the need for runtime checking. However, if there are empty spots in the long instruction word, the execution unit may still experience some waste.

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7. SIMD and Hardware Multithreading

7. SIMD and Hardware Multithreading

The video explains two ways to address parallelism challenges: Single Instruction, Multiple Data (SIMD) and hardware multithreading (SMT). SIMD allows hardware instructions to execute in parallel upon multiple data elements, simplifying scheduling and decoding logic. SMT exploits thread-level parallelism by running independent instruction streams simultaneously, demanding additional register files and careful cache sharing. The video also discusses implementing time sliced thread scheduling, where threads take turns occupying the processor's data path in round-robin fashion, reducing latency and allowing multiple threads to access computing units and memory systems simultaneously. Ultimately, the processor can accommodate as many threads as required, though performance gain may not be as significant on a single-thread processor.

• 00:00:00 In this section, the concept of Single Instruction Multiple Data (SIMD) is explained, where hardware instructions can be used for parallel execution of operations on multiple data elements. SIMD simplifies scheduling and decoding logic, as only one instruction needs to be scheduled to apply the same operation to every element of the data. The use of SIMD is beneficial if the problem being solved involves significant data parallelism, otherwise, it may not be an efficient solution. Additionally, the similarity of SIMD and vector computation is explained, where vector instructions can be used on data that is scattered across different parts of memory to perform virtual operations.
  
  
• 00:05:00 In this section, the video explains how hardware multi-threading or simultaneous multi-threading (SMT) can be used to exploit thread level parallelism. By putting multiple programs or threads on the same processor, different instruction streams can run simultaneously if they are independent of each other. Hardware multi-threading can be useful for large machines and single CPUs, as it allows for the easy extraction of instructions from two independent threads. However, this requires additional register files and careful consideration of how caches are shared among the different threads.
  
  
• 00:10:00 In this section of the video, the presenter discusses how to keep the arithmetic logic unit (ALU) busy by implementing time sliced thread scheduling. This method allows threads to take turns occupying the processor's data path in a round-robin fashion, which significantly reduces latency by scheduling more threads. By executing the next instruction while waiting for a stalled thread, this method allows for computing units and memory systems to be used by multiple threads simultaneously. Ultimately, the result is that there is no time waste, and the processor can accommodate as many threads as required. However, the performance of a single thread on a single-thread processor may not be improved as much as on a multi-thread processor.

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8. Multicore Processor Architecture

8. Multicore Processor Architecture

This video explains the architecture of multicore processors and their benefits, such as multiple cores operating independently and sharing some components, while each core has its own pipeline and data cache. The importance of cache hierarchy in bridging the speed gap between microprocessor and memory access is highlighted using multiple levels of caches that exploit temporal and spatial locality. The video also touches on system-on-chip design, which combines different function units and interfaces into a single chip to reduce cost and form factor. Overall, the video provides a useful introduction to the complexity and tradeoffs involved in designing multicore processors.

• 00:00:00 In this section, we learn about multicore processors and their advantages over single-core processors. Multicore processors have multiple cores, which operate independently and share some components such as instruction fetch, decode, and floating-point schedulers. However, each core has its own pipeline and its own level 1 data cache. Multicore processors require cache coherence protocols to ensure the data stored in each cache is consistent with data in the main memory. System-on-chip is another design option where multiple elements are combined into a single device on the same chip to reduce cost and form factor. It has a lot of different dedicated function units and interfaces connected to the rest of the chip through an unship interconnect.
  
  
• 00:05:00 In this section, the speaker explains the concept of cache hierarchy in a multicore processor architecture. The main reason for using cache is to bridge the speed gap between the microprocessor and memory access, which run at different frequencies and have different storage capabilities. The level-1, level-2, and level-3 caches are used to bridge the gap, and they exploit the temporal and spatial locality of accessing a small number of instructions or data in a short period or accessing nearby locations, respectively. The caches are organized using blocks, which are moved between the memory levels, allowing the processor to take advantage of the temporal and spatial locality.

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9. GPU Architecture

9. GPU Architecture

The accelerated processing unit (APU) is a heterogeneous processor with low power cores and GPU units all on the same chip. GPUs have a large number of shader cores that can be scheduled with instructions, and their caches are generally non-coherent, making their design simpler and allowing for much higher performance when many cores are operating at the same time. AMD and Nvidia utilize small compute units to support the operation on multiple pieces of data at the same time and have large register files to support fast context switching. The speaker also explains how to manage control flow in GPU architecture, especially in dealing with branch instructions that may produce invalid results, but programmers don't need to worry much about these issues because the processor vendors have already provided control logic in the hardware. Overall, GPUs are popular processors for complex workloads in the modern market, especially in the AI and machine learning field.

• 00:00:00 In this section, we learn about the accelerated processing unit (APU), which is a typical heterogeneous processor consisting of low power cores used for general-purpose computing, management, and configuration of the system, and GPU units, all on the same chip. The APU has a typical x86 core with two levels of cache, whereas the GPU cores are different and not x86, with lower local data storage considered private memory. Memory ownership is visible in a diagram, showing the locations, accessibility, and size of memory, with private memory smaller than local memory, and much smaller than global memory. The current laptop processors from Intel or AMD typically have a small number of general-purpose cores, integrated memory, and an on-chip graphics processing unit.
  
  
• 00:05:00 In this section, we learn about GPU architecture and how it's used in the AI and machine learning field. GPUs have a large number of processors, called shader cores, that can be scheduled with instructions. They can access high-speed GPU memory that's different from the memory subsystem of a general-purpose processor, and their caches are generally non-coherent. GPUs don't use cache coherence protocol to ensure consistency, which makes the design simpler and allows for much higher performance when many cores are operating at the same time. Overall, GPUs are a popular processor for complex workloads in the modern market.
  
  
• 00:10:00 In this section, we learn about how AMD and Nvidia utilize small compute units that have cmd hardware to support the operation on multiple pieces of data at the same time. Both vendors have 16 wide cmds, and they can group the work items into larger groups and map them to different batches depending on the chip and configuration parameters. Furthermore, they have large register files to support fast context switching, and both vendors have a combination of automatic level one cache and user-managed scratchpad, which is heavily backed in high bandwidth to support fast operations on their respective private memories. Lastly, we briefly touch on control flow and the concept of executing multiple data with one instruction, and how branch instructions cause some deviation in executing paths, requiring a way to minimize the amount of computation performed on invalid data.
  
  
• 00:15:00 In this section, the speaker explains how to manage control flow in GPU architecture, especially in dealing with branch instructions that may produce invalid results. To handle this issue, cmd lanes can be masked to discard some computations, although it may hurt the performance. Another issue that we need to address when using single instruction multiple threads (SIMT) technology is the divergence between software threads, which may waste computation cycles if not managed well. Fortunately, OpenCL programmers do not need to worry much about these issues because the processor vendors have already provided control logic in the hardware.

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10. FPGA Internals

10. FPGA Internals

This video discusses the architecture and features of field-programmable gate arrays (FPGAs). FPGAs have programmable logic, allowing them to be reprogrammed to accommodate new functionalities, and have direct access to data through massive amounts of inputs and outputs (I/Os). The lookup table structure in FPGAs consists of multiple levels of multiplexers that can be programmed to define logic functions. FPGAs use programmable registers that can be used for counters, shift registers, state machines, and DSP functions. Each rectangular block on the chip represents a Logic Array Block (LAB), with each LAB containing ten Adaptive Logic Modules (ALMs). FPGAs are used in industries such as consumer devices, automotive, medical instrumentation, and communication and broadcast.

• 00:00:00 In this section, the basics of FPGAs are introduced, including their programmable logic that can be reprogrammed to accommodate new functionalities, and their direct access to data through massive amounts of I/Os. The advantages of FPGAs are their convenient connection to memory components and coprocessors, replaceable functionalities without the need to replace hardware, and their ubiquity in industries such as consumer devices, automotive, medical instrumentation, and communication and broadcast. The architecture of FPGAs includes the lookup table, which can be programmed to define logic functions, and other important parts such as carry-in registers and adaptive logic modules that will be discussed in the subsequent sections.
  
  
• 00:05:00 In this section of the video, the presenter explains the structure of a lookup table in FPGAs, which consists of multiple levels of multiplexers with selectable inputs. The inputs to the lookup table can be used to build an arbitrary combinational logic function. The video then discusses programmable registers, which are storage elements in FPGAs used for counters, shift registers, state machines, and DSP functions. These registers have a clock signal typically driven by a global clock and can feed back into the lookup table. Additionally, the video explains how logic elements and adaptive logic modules are connected through chain signals
  and carry bits, and how the input to registers can come from previous logic elements.
  
  
• 00:10:00 In this section, we learn how to organize the logic elements inside an FPGA chip. Each rectangular block on the chip represents a Logic Array Block (LAB), with each LAB containing ten Adaptive Logic Modules (ALMs). Each ALM consists of arithmetic units, local interconnect and registration connections, as well as dedicated resources and an adaptive lookup table for flexible organization of inputs and specific output generation. Furthermore, we have row and column interconnects that can connect specific LABs, and routing scales linearly depending on the device density. Finally, we have embedded memory blocks that support different types of memory structures, such as single or dual port RAM, read-only memory, or small slices of memory structures called eMLABs.
  
  
• 00:15:00 In this section, we learn about the different functionalities of FPGAs, including digital signal processing (DSP) blocks and input/output (IO) components. DSP blocks are useful for implementing signal processing functions such as FFT transformations and high-performance multiply and accumulate operations. FPGAs can also communicate with various IO components, allowing for output enabling, controlling, and termination. The IO element logic includes bi-directional pins and output enable signal control, and the output path generates values A and B through the clock signal. On the other hand, if the output control is disabled, the input signals go through the input register.
  
  
• 00:20:00 In this section on FPGA internals, the importance of clock signals is highlighted along with the use of dedicated input clock pins to support FPGA operations. High-speed transceivers are also discussed, which are useful for implementing more complex signal protocols in FPGAs, without the need for complex signal conditioning and processing. The use of PLLs to generate different clock signals for use throughout the device with minimal skew is also explained, along with the use of SRAM cell technology and look-up tables to configure programmable bits that control the connectivity of input and output signals. The video also covers methods used to program FPGAs using external W prom, Cpl D, or a CPU to control programming sequences, and the use of a special Hardware JTAG connection for further diagnosis and debugging.
  
  
• 00:25:00 In this section, the design and features of a Field-Programmable Gate Array (FPGA) are discussed. The majority of the area in an FPGA is the logic array blocks which can be connected through row and column interconnects. Other blocks include PR, transceivers, and a memory controller that can be connected to different memory components. A PJ area 10g X FPGA is used as an example with 1 million logic elements, 1.7 million registers, 54,000 memory blocks with 22 bits each, 1,518 DSP blocks, 367.4 gigabits per second transceivers, two hard PCIe blocks, 492 general IO pins, and 12 memory controllers. The advantage of FPGAs is the high density to create complex functions, integration of multiple functions, access to different IO standards and features, and direct access to data in one chip.

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11. OpenCL Memory on a GPU System

11. OpenCL Memory on a GPU System

The instructor explains the mapping of OpenCL memory to AMD GPU and the different tiers of memory in a GPU system. The compute device has a command processor that manages directives to the compute units, which function as cores with multiple SIMD lanes, private register files, and private memory. The kernel program is intended to provide autonomous jobs that enable all the available cores to be utilized and decrease memory access latency. The speaker also mentions the concept of arithmetic intensity, which refers to the ratio between computation and memory axis movement, and how it should be high to avoid the GPU's memory bandwidth being the limiting factor.

• 00:00:00 In this section, the speaker discusses OpenCL in relation to the hardware architecture of different processors, specifically focusing on the AMD GPU. The diagram shows the global memory, constant memory, and local memory, with the kernel on the left representing the OpenCL kernel used when designing a data parallel program. Each work group performs a subset of the computation based on the data, with the local memory being local to the work group and shared with the execution units within the processor. The work items within the work group are the actual computation performed, with each having their own data set.
  
  
• 00:05:00 In this section, the instructor discusses how OpenCL memory is mapped to AMD GPU and the different levels of memory on a GPU system. The compute device has a command processor that schedules instructions to the compute units, which have level 1 cache and share some level 2 cache, and are connected to global memory. The computer unit functions as a core, with multiple SIMD lanes and private register files, called GPRs, that constitute the private memory. The kernel program is expected to provide independent pieces of work, which should be as many as possible to make all available cores busy, and these pieces should allow for hardware context switching to minimize memory access latency. The kernel should also have high arithmetic intensity to make efficient use of the hardware.
  
  
• 00:10:00 In this section, the speaker discusses the concept of romantic intensity which is essentially the ratio between computation and memory axis movement. The goal is to have math operations to memory accesses as high as possible to avoid being limited by memory bandwidth.

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12. OpenCL Example: Matrix Multiplication

12. OpenCL Example: Matrix Multiplication

This video introduces matrix multiplication as an example of OpenCL programming. The speaker demonstrates how C code is written to create independent loops that can traverse matrix rows and columns. Work items are discussed and how they can be mapped to matrix elements in OpenCL. A kernel implementation is explained, covering the kernel function arguments, how it's called, and its body. The speaker shows how the input matrix is stored into a single-dimension array using row and index numbers to calculate indices. Ultimately, the kernel function calculates the dot product to produce the element in the output matrix. The linear approach to storing matrices in physical memory is emphasized.

• 00:00:00 In this section, we learn about matrix multiplication as an example of OpenCL programming. Matrix multiplication is a classic power computing example that has been used in many different applications. The implementation requires nested loops, with the requirement that the number of columns of matrix A has to be equal to the number of rows of matrix B. This is because each resulting element in the matrix C is the dot product of a row vector of A with a column vector of B. We see how C code is written to implement the operation and how the loops work independently, allowing us to go through every row or column of the resulting matrix C in random fashion.
  
  
• 00:05:00 In this section, the concept of work items is introduced, and it is explained how work items can be mapped to matrix elements in OpenCL. Work items can be created for each output element of a matrix to be computed independently, and therefore, they can be mapped to two-dimensional range work items. The kernel implementation for the matrix multiplication in OpenCL is also discussed, where the arguments for the kernel function and how to call it from the main function are explained, and the body of the kernel function is presented. The kernel function calculates the dot product of a row vector and a column vector to calculate each element of the output matrix.
  
  
• 00:10:00 In this section, the speaker explains the process of multiplying matrices using the OpenCL programming language. The main idea is to store the two-dimensional input matrix into a single dimension array using the row number and index number to calculate the right index to find the right element to perform the dot product operation. The kernel function initializes the sum variable with 0 and iterates through the row vector of A and column vector of B to calculate the dot product, ultimately assigning the result into the corresponding element in C. These steps illustrate how to use row and column numbers to calculate indices in a linear fashion, which is essential for storing the matrix in physical memory.

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13. Structure of an OpenCL Program (part1)

13. Structure of an OpenCL Program (part1)

In the video "Structure of an OpenCL Program (part1)", the process of building an OpenCL application is explained. The program must first query the OpenCL platform to understand its resources and create an OpenCL context and command queue. Buffers are then created for data exchange between the host and device memory, and the kernel program is compiled into a binary for execution on the device. The video goes on to explain how to create read-only and write-only buffers, allocate space for output matrices, and copy results back to the host. The importance of checking API calls for successful execution is stressed.

• 00:00:00 In this section, the speaker explains the steps to build an OpenCL application. First, the program needs to query the OpenCL platform to understand the resources available on the platform, and then create an OpenCL context and command queue, which is essential for buffer operations and kernel launches. Buffers are then created to exchange data between the host and device memory. Following this, the kernel program needs to be compiled into a binary that can be executed on the accelerator device either on an FPGA or a GPU. The compilation process differs depending on the device.
  
  
• 00:05:00 In this section, the video discusses how to set up the environment to create the platform and device, create a context, and create command queues for OpenCL programming. This involves getting the platform ID, which allows the programmer to determine the number of available platforms and allocate space for storage. The video goes on to explain how to choose the devices within the platform, get information about the chosen device, set the proper arguments, and pass the values of these arguments to the kernel function to instantiate the kernel. Finally, they show how to copy the results back from the device to the host's memory once the kernel is completed.
  
  
• 00:10:00 In this section, the video explains how to create an OpenCL context and the importance of the context object in tying together all the resources necessary for OpenCL operations, such as command queues and buffers. The transcript outlines how to create read-only and write-only buffers, and how to copy data between the host and the device using CL ink and CL in Q write buffer commands. The example used is matrix multiplication, where matrices A and B are input, and matrix C is an output. The video stresses the importance of checking for successful API calls.
  
  
• 00:15:00 In this section, the speaker explains how to allocate space for Matrix C, which is the output matrix. They say that buffer C is declared as CL memory, which allows the device to write the results into it. However, this does not prohibit reading this buffer from the host side, which is necessary to retrieve the results from the device and copy the resulting matrix to somewhere in the host. The speaker shows the full definition of the buffer API, which takes five arguments: context, flags, size, host pointer, and return value.

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14. Structure of an OpenCL Program (part2)

14. Structure of an OpenCL Program (part2)

The third step in OpenCL programming involves kernel compilation, which is different for FPGA devices since it's done offline. The CL create program with source and C TX is used to create a program, followed by CL build program to build the program into binary. The correct kernel function is selected using the appropriate entry point, and kernel arguments must be initialized using CL set kernel argument with the correct pointer. The speaker goes into detail about setting up the arguments properly in matrix multiplication. They then discuss setting up local and global workgroup sizes, executing the kernel, and obtaining the results using the CL in queue buffer API. Finally, the speaker briefly mentions events in OpenCL programming.

• 00:00:00 In this section, the third step in an OpenCL program is discussed, which involves kernel compilation. This process is a bit different for FPGA devices, as the compilation is done offline. Assuming that the program source code is stored in a character buffer, the CL create program with source and C TX is used to create a program, and CL build program is used to build the program into binary. Next, the correct kernel function is selected from the source code by creating a kernel using the appropriate entry point for the specific kernel function chosen. Once the kernel is created, kernel arguments must be initialized properly using CL set kernel argument, with a pointer pointing to the actual value. For example, in matrix multiplication, seven arguments must be set up properly, including the destination buffer, the size of the matrices, and the two input matrices.
  
  
• 00:05:00 In this section, the speaker talks about initializing multiple kernel arguments and highlights the importance of correctly setting up the indices of these arguments to avoid errors. They then explain how to set local and global workgroup sizes, specifying the number of work items in a group and the number of work groups. Finally, they describe the steps to execute the kernel, including calling the OpenCL API and obtaining the results from the device to host memory using the CL in queue buffer API. The speaker also briefly mentions events and how they can be used in OpenCL programming, but this will be discussed further in later lectures.

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15. OpenCL Matrix Multiplication Demo

15. OpenCL Matrix Multiplication Demo

The "OpenCL Matrix Multiplication Demo" video explains the process of running a matrix multiplication example using OpenCL framework. It includes multiple source code files such as a main C program for the host-side, kernel program, and a makefile. The video covers different aspects of OpenCL framework, obtaining platform and device IDs, creating an OpenCL context, program and kernel objects, buffer management for the host, and creating and initializing buffers on the device. The presenter also shows a sample kernel that performs dot product operations and a demo of the final result on an AMD Radeon pro 575 compute engine.

• 00:00:00 In this section, the speaker explains how to run a matrix multiplication example through OpenCL. The example consists of several source code files, including a main C program as host-side program, a kernel program named my kernel CL, and a makefile to help compile the project. The main program includes standard libraries, macro definitions for OpenCL framework, and declarations for input matrices (matrix A and matrix B) as well as device name, platform identifiers, and numbers of devices. The speaker also describes various aspects of the OpenCL framework, such as context, program, kernel, and source code reading and compiling. Furthermore, the speaker explains the importance of platform and device IDs as well as matrix dimensions in the code.
  
  
• 00:05:00 In this section, the speaker discusses the process of obtaining the platform and device IDs and creating the OpenCL context for the matrix multiplication demo. They explain how the platform count is returned and the array is allocated to store the platform IDs. They also show how to get the device IDs for a chosen specific type and query its name. The video demonstrates how to create a command queue for each device and how to compile the OpenCL program. They further explain how to use the app open to open the kernel source code file and compile the program.
  
  
• 00:10:00 In this section, the video explains how to create a program object from the OpenCL kernel source code. This process is different on different platforms. On Mac OS with native OpenCL support, one can create a program object using the source code. On the Altera FPGA OpenCL SDK, however, creating a program object involves compiling the kernel and creating it from the binary result of that compilation using Altera's specific API. Once the program object is created, the video shows how to build the kernel program and create the kernel object. Finally, the video goes into buffer management on the host side, where a buffer is allocated to store the resulting matrix C.
  
  
• 00:15:00 In this section, the presenter explains how to create and initialize buffers on the device side for matrix multiplication using OpenCL. They go on to show how to set kernel arguments properly, including setting global and local workgroup sizes. The importance of checking the return value of the CL is also highlighted. The presenter then demonstrates how to read the results to the host memory, followed by freeing up the resources allocated on the host and OpenCL. Finally, they show a sample kernel, which uses get global ID to iterate through the width of a matrix, perform dot product operations, and store the results in the corresponding element in matrix C.
  
  
• 00:20:00 In this section, the speaker discusses building the main C program and the Mike Rinder CL program. To build the host side program, users need to compile a single C program file, and for the kernel, one can use the GPU compiler to compile the Micra know the CL to a GPU binary. After building the program on both the host and device sides, users have an executable named "main," along with different binaries available for different GPU versions. Upon executing this file, the speaker shows an OpenCL platform with an AMD Radeon pro 575 compute engine that has an initial value of the C matrix with all elements containing eight points.