LGP.png' alt='Sdk Build Tools 21.1.1' title='Sdk Build Tools 21.1.1' />Open. CL Optimization Guide AMDPreface. Developers also can generate IL and ISA code from their Open. CL kernel. About This Document. This document provides useful performance tips and optimization guidelines for programmers who want to use AMD Accelerated Parallel Processing to accelerate their applications. Contact us anytime for questions about WebEx web conferencing and collaboration products. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get. Audience. This document is intended for programmers. It assumes prior experience in writing code for CPUs and an understanding of work items. A basic understanding of GPU architectures is useful. It further assumes an understanding of chapters 1, 2, and 3 of the Open. CL Specification for the latest version, see http www. Related Documents. The Open. CL Specification, Version 1. AndroidDevTools Android Android SDKAndroid Android. Mono is a free and opensource project led by Xamarin, a subsidiary of Microsoft formerly by Novell and originally by Ximian, and the. NET Foundation, to create an. Android SDK platform. In this paper we add two more analysis tools to our earlier NER repertoire. Finnish Semantic Tagger FST is not a NER tool as such it has first and foremost been. Published by Khronos Open. CL Working Group, Aaftab Munshi ed., 2. AMD, R6. 00 Technology, R6. Instruction Set Architecture, Sunnyvale, CA, est. This document includes the RV6. GPU instruction details. ISOIEC 9. 89. 9 TC2 International Standard Programming Languages CKernighan Brian W., and Ritchie, Dennis M., The C Programming Language, Prentice Hall, Inc., Upper Saddle River, NJ, 1. I. Buck, T. Foley, D. Horn, J. Sugerman, K. Fatahalian, M. Houston, and P. Hanrahan, Brook for GPUs stream computing on graphics hardware, ACM Trans. Graph., vol. 2. 3, no. AMD Compute Abstraction Layer CAL Intermediate Language IL Reference Manual. Published by AMD. Buck, Ian Foley, Tim Horn, Daniel Sugerman, Jeremy Hanrahan, Pat Houston, Mike Fatahalian, Kayvon. Brook. GPU http graphics. Buck, Ian. Brook Spec v. Files/images/6f5/845/05f/6f584505fedf85e9624ffedbc03e65f8.jpg' alt='Sdk Build Tools 21.1.1' title='Sdk Build Tools 21.1.1' />October 3. Open. GL Programming Guide, at http www. Microsoft Direct. E44.png' alt='Sdk Build Tools 21.1.1' title='Sdk Build Tools 21.1.1' />Organization. See OpenCL Performance and Optimization is a discussion of general performance and optimization considerations when programming for AMD Accelerated. X Reference Website, at http msdn. GPGPU http www. Stanford Brook. GPU discussion forum http www. Global Memory Optimization 3 1. Two Memory Paths 3 3. Performance Impact of Fast. Path and Complete. Path 3 3. Determining The Used Path 3 4. SDK_Manager.png' alt='Sdk Build Tools 21.1.1' title='Sdk Build Tools 21.1.1' />Channel Conflicts 3 6. Staggered Offsets 3 9. Reads Of The Same Address 3 1. Float. 4 Or Float. Coalesced Writes 3 1. Alignment 3 1. 43. Summary of Copy Performance 3 1. Local Memory LDS Optimization 3 1. Constant Memory Optimization 3 1. Open. CL Memory Resources Capacity and Performance 3 2. Using LDS or L1 Cache 3 2. NDRange and Execution Range Optimization 3 2. Hiding ALU and Memory Latency 3 2. Resource Limits on Active Wavefronts 3 2. GPU Registers 3 2. Specifying the Default Work Group Size at Compile Time 3 2. Local Memory LDS Size 3 2. Partitioning the Work 3 2. Global Work Size 3 2. Local Work Size Work Items per Work Group 3 2. Moving Work to the Kernel 3 2. Work Group Dimensions vs Size 3 3. Optimizing for Cedar 3 3. Summary of NDRange Optimizations 3 3. Using Multiple Open. CL Devices 3 3. 23. CPU and GPU Devices 3 3. When to Use Multiple Devices 3 3. Partitioning Work for Multiple Devices 3 3. Synchronization Caveats 3 3. GPU and CPU Kernels 3 3. Contexts and Devices 3 4. Instruction Selection Optimizations 3 4. Instruction Bandwidths 3 4. AMD Media Instructions 3 4. Math Libraries 3 4. VLIW and SSE Packing 3 4. Compiler Optimizations 3 4. Clause Boundaries 3 4. Additional Performance Guidance 3 4. Loop Unroll pragma 3 4. Memory Tiling 3 4. General Tips 3 4. Guidance for CUDA Programmers Using Open. CL 3 5. 13. 1. 0. Guidance for CPU Programmers Using Open. CL to Program GPUs 3 5. Optimizing Kernel Code 3 5. Using Vector Data Types 3 5. Local Memory 3 5. Using Special CPU Instructions 3 5. Avoid Barriers When Possible 3 5. Optimizing Kernels for Evergreen and 6. XX Series GPUs 3 5. Clauses 3 5. 3Remove Conditional Assignments 3 5. Bypass Short Circuiting 3 5. Unroll Small Loops 3 5. Avoid Nested if s 3 5. Experiment With do while for Loops 3 5. Do IO With 4 Word Data 3 5. Index. 1. 1 Memory Bandwidth in GBs R read, W write in GBs 1 1. Open. CL Memory Object Properties 1 1. Transfer policy on cl. Enqueue. Map. Buffer cl. Enqueue. Map. Image cl. Enqueue. Unmap. Mem. Object for Copy Memory Objects 1 2. CPU and GPU Performance Characteristics 1 3. CPU and GPU Performance Characteristics on APU 1 3. Hardware Performance Parameters 2 1. Effect of LDS Usage on WavefrontsCU1 2 2. Instruction Throughput OperationsCycle for Each Stream Processor 2 2. Resource Limits for Northern Islands and Southern Islands 2 3. Bandwidths for 1. C Program To Convert Hexadecimal To Decimal more. D Copies 3 4. 3. Bandwidths for Different Launch Dimensions 3 8. Bandwidths Including float. Bandwidths Including Coalesced Writes 3 1. Bandwidths Including Unaligned Access 3 1. Hardware Performance Parameters 3 2. Impact of Register Type on WavefrontsCU 3 2. Effect of LDS Usage on WavefrontsCU 3 2. CPU and GPU Performance Characteristics 3 3. Instruction Throughput OperationsCycle for Each Stream Processor 3 4. Native Speedup Factor 3 4. Open. CL Performance and Optimization. This chapter discusses performance and optimization when programming for AMD Accelerated Parallel Processing APP GPUcompute devices, as well as CPUs and multiple devices. Details specific to the Southern Islands series of GPUs is at the end of the chapter. Code. XL GPU Profiler. The Code. XL GPU Profiler hereafter Profiler is a performance analysis tool that gathers data from the Open. CL run time and AMD Radeon GPUs during the execution of an Open. CL application. This information is used to discover bottlenecks in the application and find ways to optimize the applications performance for AMD platforms. The following subsections describe the modes of operation supported by the Profiler. Collecting Open. CL Application Traces. This mode requires running an application trace GPU profile sesstion. To do this Sample Application Trace API Summary. Timeline View. The Timeline View See Sample Timeline View provides a visual representation of the execution of the application. Sample Timeline View. At the top of the timeline is the time grid it shows, in milliseconds, the total elapsed time of the application when fully zoomed out. Timing begins when the first Open. CL call is made by the application it ends when the final Open. CL call is made. Below the time grid is a list of each host OS thread that made at least one Open. CL call. For each host thread, the Open. CL API calls are plotted along the time grid, showing the start time and duration of each call. Below the host threads, the Open. CL tree shows all contexts and queues created by the application, along with data transfer operations and kernel execution operations for each queue. You can navigate in the Timeline View by zooming, panning, collapsingexpanding, or selecting a region of interest. From the Timeline View, you also can navigate to the corresponding API call in the API Trace View, and vice versa. The Timeline View can be useful for debugging your Open. CL application. Examples are given below. The Timeline View lets you easily confirm that the high level structure of your application is correct by verifying that the number of queues and contexts created match your expectations for the application. You can confirm that synchronization has been performed properly in the application. For example, if kernel A execution is dependent on a buffer operation and outputs from kernel B execution, then kernel A execution must appear after the completion of the buffer execution and kernel B execution in the time grid. It can be hard to find this type of synchronization error using traditional debugging techniques. You can confirm that the application has been using the hardware efficiently.