Collecting OpenCL*-related Metrics with Intel Graphics Performance Analyzers

Transcription

1 Collecting OpenCL*-related Metrics with Intel Graphics Performance Analyzers

2 Collecting Important OpenCL*-related Metrics with Intel GPA System Analyzer Introduction Intel SDK for OpenCL* Applications 2012 is a comprehensive software development environment for OpenCL visual computing applications on 3rd generation Intel Core processor family-based platforms. Starting with the 2012 release, Intel SDK for OpenCL Applications supports the OpenCL 1.1 full profile on 3rd gen Intel Core processors with Intel HD Graphics 4000/2500. The SDK is available at Optimizing performance for Intel HD Graphics devices is discussed in the Intel SDK for OpenCL* Applications 2012 OpenCL* Optimization Guide. The SDK also provides integration with the Intel Graphics Performance Analyzers (Intel GPA), which enables you to optimize and analyze your OpenCL code in visual computing applications. Intel GPA support various metrics for Intel CPU and HD Graphics devices. Some metrics are specific to the rendering (Microsoft DirectX* API) pipeline only, while some are more general and can be associated with OpenCL execution. With Intel GPA you can also inspect various important hardware counters for Intel CPU and HD Graphics devices in real time, for example: Utilization of CPU cores and the execution units in Intel HD Graphics devices Memory traffic for Intel HD Graphics devices Power consumption, and so on This article provides an overview of the OpenCL-related metrics for Intel HD Graphics devices. For CPU metrics, more fine-grain methods exist, for example, using Intel VTune Amplifier XE software. For details on profiling for OpenCL on CPU see the Tuning with the Intel VTune Amplifier XE chapter of the Intel SDK for OpenCL* Applications User s Guide. You can also check the overall CPU utilization and job distribution with the Intel GPA, as detailed in section Using the Intel Graphics Performance Analyzers (Intel GPA) Platform Analyzer also in the User s Guide. Intel GPA System Analyzer To get access to metrics you should run your application with Intel GPA Monitor first. Then you should use the Intel GPA System Analyzer Heads-up Display (HUD), which displays metrics in panels on top of your application (Figure 1, left). Alternatively, you open the standalone version of the System Analyzer in a separate window (Figure 2, right). See the Intel Graphics Performance Analyzers 2012 R3 Online help for details. When you run the HUD version of the Intel GPA System Analyzer (by pressing Ctrl- F1), you can only display four metrics simulateneously. You can select the metrics to display in the Profile settings of the Intel GPA Monitor, Figure 1 right. 2

3 Intel GPA System Analyzer When you run the standalone version of the Intel GPA System Analyzer (Figure 2), you can display many metrics at the same time. Running the standalone version of the System Analyzer does not load the GPU with its own GUI rendering, making the standalone version preferable when potential overheads, associated with the System Analyzer itself, are of concern. You can use it over a network connection, although local profiling (with dual computer monitors or non-full screen applications) is possible. Be aware that the overhead of collecting the metrics may impact your measurements, so look at any data you collect carefully to make sure it makes sense. See Running the Intel GPA System Analyzer in Local and Network Modes section of the Intel Graphics Performance Analyzers 2012 R3 Online help for details. Figure 1. Left: Intel GPA System Analyzer HUD (press Ctrl-F1 to switch the modes). Right: HUD metrics for a selected Intel GPA Monitor profile. 3

4 Collecting Important OpenCL*-related Metrics with Intel GPA System Analyzer Figure 2. Standalone version of the Intel GPA System Analyzer. Left: connection to the machine (use localhost to connect to the local machine). Right: List of applications available for profiling. After you connect to the machine and select the application to profile, the main window of the analyzer appears (Figure 3). Figure 3. Intel GPA System Analyzer in action Limitations When profiling non-directx applications, only CPU metrics are available. The reason is that even general GPU metrics are queried as deltas within each Direct3D* frame. Thus, you cannot view any GPU related metrics for console applications. 4

5 OpenCL*-related Metrics OpenCL*-related Metrics The following types of OpenCL-related metrics are supported: CPU-specific metrics, such as core utilization HD Graphics execution units (EUs) metrics, of which GPU EUs active/idle/stalled are the most important Memory metrics, such as GPU memory reads/writes Power metrics for CPUs, Intel HD Graphics devices, and the whole package You can locate them in the metrics tree at the left side of the Intel GPA System Analyzer (Figure 4). The GPU busy metric does not include general computations, like OpenCL, so it actually means GPU Busy within 3D rendering contexts. See the Intel Graphics Performance Analyzers 2012 R3 Online help for details. Figure 4. List of metrics available in Intel GPA System Analyzer 5

6 Collecting Important OpenCL*-related Metrics with Intel GPA System Analyzer Commonly Used Metrics The screenshot of the System Analyzer tool in Figure 3 shows the available metrics on the left. The active (drag-n-dropped from the list) metrics are displayed in charts. In this section we describe some of the metrics you will commonly use. Isolating OpenCL* Tasks Generally, metrics are collected on the entire system. Hence, tasks like rendering, desktop/windows* updates, and general computations with OpenCL are collected together. Try to minimize the influence of rendering when debugging OpenCL performance for Intel HD Graphics devices. Various video activities (for example, Intel Quick Synchronization Video or hardware-assisted transcoding with Intel Media SDK) also influence the metrics, see the Using Intel Graphics Performance Analyzers (GPA) to analyze Intel Media Software Development Kit-enabled applications for an example analysis. You can isolate the OpenCL tasks in several ways: Temporarily remove/comment expensive Microsoft DirectX calls. If possible, skip intensive rendering with a simple change of camera direction or position, for example to make the most of scene out-of-view frustum. Zoom out so that just a few pixels stay processed with expensive pixel shaders. Estimate or minimize the number of pixels with Pixels Rendered metric. Similarly, you can minimize the influence of video transcoding by disabling the decoding (and working with a single frame), etc. See the OpenCL* and Intel Media SDK Interoperability sample, which enables you to pause/resume decoding and processing with OpenCL with simple GUI. You can experiment with it using the System Analyzer to understand the contribution to the metrics of the different samples pipeline stages. Frame Time The frame time metric represents the instantaneous frame time in microseconds. When you choose this metric, the information on frames duration displays (see the screenshot in Figure 5). 6

7 Commonly Used Metrics Figure 5. Frame time metric This Frame Time metric is for the DirectX frame (for example, time between Present calls). Refer to the section above ( Isolating OpenCL* tasks ) on general recommendations for a careful breakdown of frame costs. General Execution Units Metrics Unless your algorithm is memory-bound, the execution units (EUs) are likely the performance bottleneck for your application and the EU metrics can provide information on these bottlenecks. The goal is to maximize the utilization of EUs with useful computations. Refer to the OpenCL* Optimization Guide for tips and tricks. The following information briefly describes the EU-related metrics: GPU EUs Active represents the percentage of time when the GPU execution units (EUs) were actively executing. Idle is the percentage of time when the GPU execution units (EUs) were idle. An EU is idle when it is neither actively executing instructions nor stalled trying to execute shader instructions (below). GPU EUs Stalled metric represents the percentage of time when the GPU execution units (EUs) were stalled. An EU becomes stalled when all of its threads are waiting for results from fixed function units, for example, requesting data from the texture sampler, which can be detailed with the dedicated EUs Stalled on Samp. metric. In the screenshot in Figure 6, the percentage of GPU EUs Stalled is quite high. This might indicate inefficient memory bandwidth usage (for example, suboptimal data access granularity or cache trashing, so that the GPU waits for data to arrive). If you suspect the data stalls, you could change the kernel to increase the operations/byte ratio. 7

8 Collecting Important OpenCL*-related Metrics with Intel GPA System Analyzer Figure 6. General Execution Units Metrics Stalling might also be a result of contention for fixed function units, for example, mathbox, used for things like transcendental math. This type of stall can be detailed with EUs Stalled on Math metric. Try relaxed/native precision in this case. See the Intel Graphics Performance Analyzers 2012 R3 Online help for details. Finally, if the number of workgroups in the flight is insufficient, the EUs utilization might be really low (GPU EUs Idle will be high). Too low a value for the local size provided to the clenqueuendrange call can also result in units being idle. See the Work-Group Size Recommendations Summary section in the OpenCL* Optimization Guide for details. Memory-Related Metrics Among all of the memory related metrics, the GPU Memory Reads/Writes are attributed to an OpenCL kernel execution. When you choose this metric, the memory traffic of your application displays, similar to the screenshot in Figure 7. Figure 7. Memory metrics For this specific example, the total (reads+writes) traffic is rather intensive, totaling ~13 GBytes/sec, which is pretty sane bandwidth saturation. See the OpenCL* Optimization Guide for theoretical memory performance and hints on saturating the bandwidth. 8

9 Summary Try to access data in large (128-bit) chunks. See the OpenCL* Optimization Guide for details. Another good optimization to relax the bandwidth pressure is using local memory. Summary The key points covered in this paper are: Intel GPA System Analyzer provides a convenient way to track the selected set of metrics (counters), both HUD or the standalone version. System Analyzer provides a set of OpenCL-related metrics that cover Intel CPU and HD Graphics devices and socket (system) utilization. For Intel HD Graphics devices, metrics for execution units and memory utilization are the most important. Refer to the OpenCL* Optimization Guide for more tips on metric values. Notices INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL'S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. UNLESS OTHERWISE AGREED IN WRITING BY INTEL, THE INTEL PRODUCTS ARE NOT DESIGNED NOR INTENDED FOR ANY APPLICATION IN WHICH THE FAILURE OF THE INTEL PRODUCT COULD CREATE A SITUATION WHERE PERSONAL INJURY OR DEATH MAY OCCUR. Intel may make changes to specifications and product descriptions at any time, without notice. Designers must not rely on the absence or characteristics of any features or instructions marked "reserved" or "undefined." Intel reserves these for future definition and shall have no responsibility whatsoever for conflicts or incompatibilities arising from future changes to them. The information here is subject to change without notice. Do not finalize a design with this information. The products described in this document may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Contact your local Intel sales office or your distributor to obtain the latest specifications and before placing your product order. 9

10 Collecting Important OpenCL*-related Metrics with Intel GPA System Analyzer Copies of documents which have an order number and are referenced in this document, or other Intel literature, may be obtained by calling , or go to: Software and workloads used in performance tests may have been optimized for performance only on Intel microprocessors. Performance tests, such as SYSmark and MobileMark, are measured using specific computer systems, components, software, operations, and functions. Any change to any of those factors may cause the results to vary. You should consult other information and performance tests to assist you in fully evaluating your contemplated purchases, including the performance of that product when combined with other products. Any software source code reprinted in this document is furnished under a software license and may only be used or copied in accordance with the terms of that license. Intel, Core, VTune, and the Intel logo are trademarks of Intel Corporation in the US and/or other countries. OpenCL and the OpenCL logo are trademarks of Apple Inc and are used by permission by Khronos. Copyright 2012 Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. 10