![]() Individual metrics which track how many times a certain event occurs in the hardware, such as bytes moved from L2 cache or a 32 bit floating point add performed Hardware agnostic methodology for quantifying a workload’s ability to saturate the given compute architecture in terms of floating-point compute and memory bandwidth Includes the AMD Instinct™ MI50/60, MI100, and MI200 series accelerators.Ī C++ Runtime API and kernel language that allows developers to create portable compute kernels/applications for AMD and NVIDIA GPUs from a single source codeĪ profiling approach where durations of compute kernels and data transfers between devices are collected and visualized Includes the RX 5000, 60 GPUs.ĪMD’s Compute dedicated GPU architecture optimized for accelerating HPC, ML/AI, and data center type workloads. Used by the AMD EPYC™, AMD Ryzen™, AMD Ryzen™ PRO, and AMD Threadripper™ PRO processor series.ĪMD’s Traditional GPU architecture optimized for graphically demanding workloads like gaming and visualization. The following terms are used in this blog post:ĪMD’s x86-64 processor core architecture design. In this introductory blog, we briefly describe the following tools that can aid in application analysis: This post covers everything from low level profiling tools to extensive profiling suites. This post serves as an introduction to the various profiling tools offered by AMD and why a developer might leverage one over the other. Developers targeting AMD GPUs have multiple tools available depending on their specific profiling needs. With AMD’s profiling tools, developers are able to gain important insight into how efficiently their application is utilizing hardware and effectively diagnose potential bottlenecks contributing to poor performance. Thus, performance tuning is a necessary component in the benchmarking process. Understanding the critical path and kernel execution is all the more important. Heterogenous systems, where programs run on both CPUs and GPUs, introduce additional complexities. However, in order to extract the most performance from emerging hardware, the program must be tuned many times and requires more than measuring raw execution time: one needs to know where the program is spending most of its time and whether further improvements can be made. These benchmarks are useful in that they provide insight into the characteristics of the application, and enables one to discover potential bottlenecks that could result in performance degradation during operational settings.Īt face value, benchmarking sounds simple enough and is often interpreted as simply a comparison of execution time on a variety of difference machines. In other words: a good benchmark should be representative of the real work that needs to be done. In practice, many application developers construct benchmarks, which are carefully designed to measure the performance, such as execution time, of a particular code within an operational-like setting. This is particularly challenging as hardware continues to evolve over time, and as a result codes may require further tuning. ![]() In many industries, it is also required that applications and their complex software stack run as efficiently as possible to meet operational demands. ![]() Getting a code to be functionally correct is not always enough. Introduction to profiling tools for AMD hardware
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