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.
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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.
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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. 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.
ISO/IEC 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. Sugerman, K. Fatahalian, M. Houston, and P. Hanrahan, “Brook for GPUs: stream computing on graphics hardware,” ACM Trans. Graph., vol. 7. 77- 7. 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. October 3. 1, 2. 00.
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. This information is used to discover bottlenecks in the application and find ways to optimize the application’s 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, collapsing/expanding, 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. For example, the timeline should show that non- dependent kernel executions and data transfer operations occurred simultaneously.
Summary Pages View. The Summary Pages View (See Sample Summary Pages View) shows various statistics for your Open.
CL application. It can give a general idea of the location of the application’s bottlenecks. It also provides useful information, such as the number of buffers and images created on each context, the most expensive kernel call, etc. Sample Summary Pages View. The Summary Pages View provides access to the following individual pages. API Summary . It also shows the number of buffers and images created for each context. Kernel Summary . From these summary pages, you can determine if the application is bound by kernel execution or data transfer (Context Summary page).
If the application is bound by kernel execution, you can determine which device is the bottleneck. From the Kernel Summary page, you can find the name of the kernel with the highest total execution time. Or, from the Top 1. Kernel Summary page, you can find the individual kernel instance with the highest execution time. If the kernel execution on a GPU device is the bottleneck, the GPU performance counters then can be used to investigate the bottleneck inside the kernel. See See Collecting Open. CL GPU Kernel Performance Counters.
Collecting Open. CL GPU Kernel Performance Counters for more details. If the application is bound by the data transfers, it is possible to determine the most expensive data transfer type (read, write, copy, or map) in the application from the Context Summary page. You can see if you can minimize this type of data transfer by modifying the algorithm. With help from the Timeline View, you can investigate whether data transfers have been executed in the most efficient way (concurrently with a kernel execution).
API Trace View. The API Trace View (See Sample API Trace View) lists all the Open. CL API calls made by the application. Sample API Trace View. Each host thread that makes at least one Open. CL call is listed in a separate tab.
Each tab contains a list of all the API calls made by that particular thread. For each call, the list displays the index of the call (representing execution order), the name of the API function, a semi- colon- delimited list of parameters passed to the function, and the value returned by the function.
When displaying parameters, the profiler tries to dereference pointers and decode enumeration values in order to give as much information as possible about the data being passed in, or returned from, the function. Double- clicking an item in the API Trace View displays and zooms into that API call in the Host Thread row in the Timeline View.
This view lets you analyze and debug the input parameters and output results for each API call. For example, you easily can check that all the API calls are returning CL. This view also lets you identify redundant API calls. Collecting Open. CL GPU Kernel Performance Counters.
To collect these counters, run a performance counters GPU profile session using the following steps. Open (or create) an Code. XL project. Select the GPU: Performance Counters Profile Type. Click the Start Code.
XL Profiling toolbar button,, to start profiling. Pause / Stop the profiled application using the execution toolbar buttons. When the profiled application execution is over, Code. XL displays the session. The GPU kernel performance counters can be used to find possible bottlenecks in the kernel execution.
You can find the list of performance counters supported by AMD Radeon. Once the trace data has been used to discover which kernel is most in need of optimization, you can collect the GPU performance counters to drill down into the kernel execution on a GPU device. Using the performance counters, we can: Find the number of resources (general- purpose registers, local memory size, and flow control stack size) allocated for the kernel. These resources affect the possible number of in- flight wavefronts in the GPU. A higher number hides data latency better.
Determine the number of ALU, as well as global and local memory instructions executed by the GPU. Determine the number of bytes fetched from, and written to, the global memory. Determine the use of the SIMD engines and memory units in the system. View the efficiency of the shader compiler in packing ALU instructions into the VLIW instructions used by AMD GPUs. View any local memory (Local Data Share – LDS) bank conflicts. The Session View (See Example Session View – Performance Counters for a Profile Session) shows the performance counters for a profile session.
The output data is recorded in a comma- separated variable format. You also can click on the kernel name entry in the “Method” column to view the Open.
CL kernel source, AMD Intermediate Language (IL), GPU ISA, or CPU assembly code for that kernel. Example Session View – Performance Counters for a Profile Session.
AMD APP Kernel. Analyzer. AMD APP Kernel. Analyzer.
Open. CL kernels for AMD GPUs. It gives accurate kernel performance estimates and lets you view kernel compilation results and assembly code for multiple GPUs, without requiring actual GPU hardware. Start Kernel. Analyzer. Kernel. Analyzer.
Code. XL. Launch Kernel. Analyzer. 2 from within Code. XL by selecting, from the Code. XL menu bar, Analyze . For Windows or Linux, the Kernel. Analyzer. 2 window appears (See Kernel. Analyzer. 2 Main Window).
Kernel. Analyzer. Main Window. The window contains three panels: a kernel source panel at top left,a kernel assembly code panel at top right, anda build output/statistics/analysis panel at bottom. Open Kernel Source. To open the kernel source, select File . For a 3. D kernel, X, Y, and Z must be supplied. For a 2. D kernel, Z must be defined as 1 or 0; for a 1.
D kernel, both Y and Z must be defined as 0 or 1. Build the Kernel. After setting the build options, press F7 or select Build . The example in See Sample Compilation Outputshows successful builds (no warnings or errors) for 1.
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