8 Profiling Glasgow Haskell Compiler 9.4.4 User’s Guide

Indeed, the celebrated 14th-century Persian pahlavan Maḥmūd Khwārezmī was both. Contests for runners and jumpers were to be found across the length and breadth of the continent. During the age of imperialism, explorers and colonizers were often astonished by the prowess of these “primitive” peoples. Nandi runners of Kenya’s Rift Valley seemed to run distances effortlessly at a pace that brought European runners to pitiable physical collapse. A child sees a flat stone, picks it up, and sends it skipping across the waters of a pond. An adult realizes with a laugh that he has uttered an unintended pun.

• Sport and performance psychologists are experts in helping athletes and professionals overcome problems that impede performance.
• In this article, we analyze the performance differences between CSS-based and JavaScript-based animation.
• The user manual for NVIDIA profiling tools for optimizing performance of CUDA applications.
• In the segment mode the timeline is split into equal width segments and only aggregated data values for each time segment are shown.
• It is recommended to use next-generation tools NVIDIA Nsight Systems for GPU and CPU sampling and tracing and NVIDIA Nsight Compute for GPU kernel profiling.
• CDP kernel launch tracing has a limitation for devices with compute capability 7.0 and higher.
• Each multiply-accumulate operation contributes 1 to the count.

It provides detailed performance metrics and API debugging via a user interface and command line tool. In addition, its baseline feature allows users to compare results within the tool. Nsight Compute provides a customizable and data-driven user interface and metric collection and can be extended with analysis scripts for post-processing results. Refer to the nvprof Transition Guide section in the Nsight Compute CLI document. Refer to the Visual Profiler Transition Guide section in the Nsight Compute document. All events and metrics for devices with compute capability 3.x and 5.0 can now be collected accurately in presence of multiple contexts on the GPU.

Sport and Performance Psychology Delivers Peak Performance

The time your application spends in a parallel region or idling is shown both on the timeline and is summarized in this view. The reference for the percentage of time spent in each type of activity is the time from the start of the first parallel region to the end of the last parallel region. The sum of the percentages of each activity type often exceeds 100% because the OpenMP runtime can be in multiple states at the same time.

The chart shows a summary view of the memory hierarchy of the CUDA programming model. The green nodes in the diagram depict logical memory space whereas blue nodes depicts actual hardware unit on the chip. For the various caches the reported percentage number states the cache hit rate; that is the ratio of requests that could be served with data locally available to the cache over all requests made. The coloring mode can be selected in the View menu, in the timeline context menu (accessed by right-clicking in the timeline view), and on the profiler toolbar. In kernel coloring mode, each type of kernel is assigned a unique color . In stream coloring mode, each stream is assigned a unique color .

7. Observing Code Coverage¶

The act of tracking people down via online tools or any other tools to find out more information about a person. Typically this word is used to describe males who are trying to find more information about females they have met or are attracted to. Not to be confused with stalking where the difference lies in the intent. Stalkers and stalking is normally used to describe people who track people down normally with evil intent, like trying to murder them, annoy them, rape them, etc…profilers are above this kind of behavoir.

If the golf coach told you your putter is swinging too far back and through it would be an extrinsic source, as the feedback is collected and analysed from outside your own senses. Master data management is a process that creates a uniform set of data on customers, products, suppliers and other business entities from different IT systems. Performance audits include economy and efficiency audits and program audits. Performance Profiles of Major Energy Producers, 2009.Figure 15 data. DiSC assessments are extensively researched and time-tested. The publisher of DiSC assessments, Wiley, is one of the world’s oldest and most respected publishers of scientific and technical references.

7.1. OpenMP Options

Note that these counters are currently not processed well be eventlog2html. So if you want to check them you will have to use the text based interface. But sometimes having information about these binders is critical.

Consider combining several texture fetch operations into one (e.g., packing data in texture and unpacking in SM or using vector loads). Stalled for memory dependency – The next instruction is waiting for a previous memory accesses to complete. For very short kernels, consider fusing into a single kernels. The blog post Track MPI Calls in the Visual Profiler shows how Visual Profiler, combined with PMPI and NVTX can give interesting insights into how the MPI calls in your application interact with the GPU. In Visual Profiler’s New Session wizard, use the Configure button to open the toolkit configuration window.

However, as of CUDA Toolkit version 10.1 Update 2, the JRE is no longer included in the CUDA Toolkit due to Oracle upgrade licensing changes. The user must install JRE 1.8 in order to use Visual Profiler. Also see Java Platform, Standard Edition 8 Names and Versions.

Using a custom timer¶

Provides the correlation ID when profiling data is generated in CSV format. The Visual Profiler supports a new option to select the PC sampling frequency. Added an option to enable/disable the OpenMP profiling in Visual Profiler. Profilers no longer turn off the performance characteristics of CUDA Graph when tracing the application. Profiling is not supported for CUDA kernel nodes launched by a CUDA Graph.

Instrumentation is key to determining the level of control and amount of time resolution available to the profilers. Our blog, Discprofiles.com, is your source for learning more about Everything DiSC and other topics. DiSC profiles level the playing field by giving trainers and trainees the non-judgmental information they need to train more effectively. DiSC profiles can also help improve your effectiveness in sales situations.

This document is not a commitment to develop, release, or deliver any Material , code, or functionality. If the new NVIDIA Tools Extension API feature of domains is used then Visual Profiler and nvprof will show the NVTX markers and ranges grouped by domain. Refer CPU Details View and CPU Source View for more information. OpenACC profiling is now also supported on non-NVIDIA systems. The Visual Profiler color codes links in the NVLink topology diagram based on throughput.

Understanding and adapting to your customers’ styles is essential for connecting on a human level and seeing each other “eye to eye.” DiSC profiles teach you how to improve communication and understanding between team members. Explore the full catalog of profiles, reports, definition of performance profiling kits, and tools. Teams are the building blocks of almost every successful organization today. Innovate faster, reduce operational cost and transform IT operations with an AIOps platform that delivers visibility into performance data and dependencies across environments.

Knowledge of performance

In addition to the guided analysis results, you will see a timeline for your application showing the CPU and GPU activity that occurred as your application executed. Read Timeline View and Properties View to learn how to explore the profiling information that is available in the timeline. Navigating the Timeline describes how you can zoom and scroll the timeline to focus on specific areas of your application. As described in the Analysis View section, you can use the guided analysis system to get recommendations on performance limiting behavior in your application. Profile execution on the CPU – If selected the CPU threads are sampled and data collected about the CPU performance is shown in the CPU Details View.

As shown in the following figure, when creating a new session or editing an existing session you can specify that the application being profiled resides on a remote system. Enable CPU thread tracing – If enabled, selected CPU thread API calls will be recorded and displayed on a new thread API timeline. This currently includes the Pthread API, mutexes and condition variables.

A bytecode, control table or JIT interpreters are three examples that usually have complete control over execution of the target code, thus enabling extremely comprehensive data collection opportunities. Flat profilers compute the average call times, from the calls, and do not break down the call times based on the callee or the context. Profiling is achieved by instrumenting either the program source code or its binary executable form using a tool called a profiler .

Gathering program events

One limitation has to do with accuracy of timing information. There is a fundamental problem with deterministic profilers involving accuracy. The most obvious restriction https://globalcloudteam.com/ is that the underlying “clock” is only ticking at a rate of about .001 seconds. Hence no measurements will be more accurate than the underlying clock.

Similarly, if you select a kernel or memcpy interval in the Timeline View the table will be scrolled to show the corresponding data. Together, the views allow you to analyze and visualize the performance of your application. This section describes each view and how you use it while profiling your application.

Note that auto boost is supported only on certain Tesla devices from the Kepler+ family. Processes are run simultaneously on the same node, there is an issue of contention for files under the temporary directory. One workaround is to set a different temporary directory for each process. When using remote profiling if there is a connection failure due to key exchange failure, then you will get an error message “Unable to establish shell connection to ‘user@ xxx’”. Visual Profiler requires Java Runtime Environment 1.8 to be available on the local system. However, starting with CUDA Toolkit version 10.1 Update 2, the JRE is no longer included in the CUDA Toolkit due to Oracle upgrade licensing changes.

Understanding Sport and Performance Psychology

Knowledge of performance allows an athlete to match their movement to an outcome. Without enough, or the correct knowledge of performance learning will be very slow and will often stop. In our putting example, imagine if the golf ball went 5 meters too far, but you had no idea how hard you hit the ball, how long your swing was, or how the ball felt when you hit it. Without this information, you are unable to update your solution for your next attempt. Knowledge of performance focuses on the information about how the action was performed.

In general, Greek culture included both cultic sports, such as the Olympic Games honouring Zeus, and secular contests. Played with carefully sewn stuffed skins, with animal bladders, or with found objects as simple as gourds, chunks of wood, or rounded stones, ball games are universal. Ball games of all sorts were quite popular among the Chinese. Descriptions of the game cuju, which resembled modern football , appeared as early as the Eastern Han dynasty (25–220).

2. Metrics for Capability 6.x

The Visual Profiler guided analysis system can now generate a kernel analysis report. The report is a PDF version of the per-kernel information presented by the guided analysis system. Both profilers allow you to see the Unified Memory related memory traffic to and from each GPU on your system. Improved source-to-assembly code correlation for CUDA Fortran applications compiled by the PGI CUDA Fortran compiler. Visual Profiler and nvprof now support OpenACC profiling.

The following example shows how the current host OS thread can be named. Zeroing the structure sets all the event attributes types and values to the default value. The version and size field are used by NVTX to handle multiple versions of the attributes structure. The sample program below shows the use of marker events, range events, and resource naming.