Intel Advisor

Last updated
Intel Advisor
Developer(s) Intel Developer Products
Stable release
2021.4 / October 1, 2021;14 months ago (2021-10-01) [1]
Operating system Windows and Linux (UI-only on macOS)
Type Profiler
License Free and commercial support
Website software.intel.com/content/www/us/en/develop/tools/oneapi/components/advisor.html

Intel Advisor (also known as "Advisor XE", "Vectorization Advisor" or "Threading Advisor") is a design assistance and analysis tool for SIMD vectorization, threading, memory use, and GPU offload optimization. The tool supports C, C++, Data Parallel C++ (DPC++), Fortran and Python languages. It is available on Windows and Linux operating systems in form of Standalone GUI tool, Microsoft Visual Studio plug-in or command line interface. [2] It supports OpenMP (and usage with MPI). Intel Advisor user interface is also available on macOS.

Contents

Intel Advisor is available for free as a stand-alone tool or as part of the Intel oneAPI Base Toolkit. Optional paid commercial support is available for the oneAPI Base Toolkit.

Features

Vectorization optimization

Vectorization is the operation of Single Instruction Multiple Data (SIMD) instructions (like Intel Advanced Vector Extensions and Intel Advanced Vector Extensions 512) on multiple objects in parallel within a single CPU core. This can greatly increase performance by reducing loop overhead and making better use of the multiple math units in each core.

Intel Advisor helps find the loops that will benefit from better vectorization, identify where it is safe to force compiler vectorization. [3] It supports analysis of scalar, SSE, AVX, AVX2 and AVX-512-enabled codes generated by Intel, GNU and Microsoft compilers auto-vectorization. It also supports analysis of "explicitly" vectorized codes which use OpenMP 4.x and newer as well as codes or written using C vector intrinsics or assembly language. [4] [5]

Automated Roofline analysis

Intel Advisor automates the Roofline Performance Model first proposed at Berkeley [6] and extended at the University of Lisbon. [7]

Roofline Performance Model automation integrated with other features in Intel Advisor. Each circle corresponds to one loop or function Roofline in Intel Advisor.png
Roofline Performance Model automation integrated with other features in Intel Advisor. Each circle corresponds to one loop or function

Advisor "Roofline Analysis" helps to identify if given loop/function is memory or CPU bound. It also identifies under optimized loops that can have a high impact on performance if optimized. [8] [9] [10] [11]

Intel Advisor also provides an automated memory-level roofline implementation that is closer to the classical Roofline model. Classical Roofline is especially instrumental for high performance computing applications that are DRAM-bound. Advisor memory level roofline analyzes cache data and evaluates the data transactions between different memory layers to provide guidance for improvement. [12]

Intel Advisor roofline analysis supports code running on CPU or GPU. [13] [14] It also supports integer based applications - that is heavily used in machine learning, big data domains, database applications, financial applications like crypto-coins. [15]

Threading prototyping

Software architects add code annotations to describe threading that are understood by Advisor, but ignored by the compiler. Advisor then projects the scalability of the threading and checks for synchronization errors. Advisor Threading "Suitability" feature helps to predict and compare the parallel SMP scalability and performance losses for different possible threading designs. Typical Suitability reports are shown on Suitability CPU screen-shot on the right side. Advisor Suitability provides dataset size (iteration space) modeling capabilities and performance penalties break-down (exposing negative impact caused by Load Imbalance, Parallel Runtimes Overhead and Lock Contention). [16]

Suitability "CPU model" IntelAdvisorSuitabilityCPU.png
Suitability "CPU model"

Offload modelling

Advisor adds GPU offload performance modeling feature in the 2021 release. It collects application performance characteristics on a baseline platform and builds analytical performance model for target (modelled) platform.

This provides performance speedup estimation on target GPUs and overhead estimations for offloading, data transfer and scheduling region execution and pinpoints performance bottlenecks. [17] [18] [19] This information can serve for choosing offload strategy: selecting regions to offload and anticipate potential code restructuring needed to make it GPU-ready.

Customer usage

Intel Advisor is used by Schlumberger, [20] Sandia national lab, and others [21] for design and parallel algorithm research and Vectorization Advisor capabilities known to be used by LRZ and ICHEC, [22] Daresbury Lab, [23] Pexip. [24]

The step-by-step workflow is used by academia for educational purposes. [25]

See also

Related Research Articles

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Single instruction, multiple data (SIMD) is a type of parallel processing in Flynn's taxonomy. SIMD can be internal and it can be directly accessible through an instruction set architecture (ISA), but it should not be confused with an ISA. SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously.

In computing, Streaming SIMD Extensions (SSE) is a single instruction, multiple data (SIMD) instruction set extension to the x86 architecture, designed by Intel and introduced in 1999 in their Pentium III series of Central processing units (CPUs) shortly after the appearance of Advanced Micro Devices (AMD's) 3DNow!. SSE contains 70 new instructions, most of which work on single precision floating-point data. SIMD instructions can greatly increase performance when exactly the same operations are to be performed on multiple data objects. Typical applications are digital signal processing and graphics processing.

In computing, a vector processor or array processor is a central processing unit (CPU) that implements an instruction set where its instructions are designed to operate efficiently and effectively on large one-dimensional arrays of data called vectors. This is in contrast to scalar processors, whose instructions operate on single data items only, and in contrast to some of those same scalar processors having additional single instruction, multiple data (SIMD) or SWAR Arithmetic Units. Vector processors can greatly improve performance on certain workloads, notably numerical simulation and similar tasks. Vector processing techniques also operate in video-game console hardware and in graphics accelerators.

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<span class="mw-page-title-main">Larrabee (microarchitecture)</span>

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AVX-512 are 512-bit extensions to the 256-bit Advanced Vector Extensions SIMD instructions for x86 instruction set architecture (ISA) proposed by Intel in July 2013, and implemented in Intel's Xeon Phi x200 and Skylake-X CPUs; this includes the Core-X series, as well as the new Xeon Scalable Processor Family and Xeon D-2100 Embedded Series. AVX-512 consists of multiple extensions that may be implemented independently. This policy is a departure from the historical requirement of implementing the entire instruction block. Only the core extension AVX-512F is required by all AVX-512 implementations.

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References

  1. ↑ "Intel® Advisor Release Notes and New Features".
  2. ↑ "Command Line Use Cases". Intel. Retrieved 2021-01-05.
  3. ↑ "Optimize Vectorization Aspects of a Real-Time 3D Cardiac..." Intel. Retrieved 2021-01-07.
  4. ↑ "HPC Code Modernization Tools" (PDF).{{cite web}}: CS1 maint: url-status (link)
  5. ↑ "Новый инструмент анализа SIMD программ — Vectorization Advisor". habr.com (in Russian). Retrieved 2021-01-05.
  6. ↑ Williams, Samuel (April 2009). "Roofline: An insightful Visual Performance model for multicore Architectures" (PDF). University of Berkeley.
  7. ↑ Ilic, Aleksandar. "Cache-aware Roofline model: Upgrading the loft" (PDF). Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento em Lisboa.
  8. ↑ "Roofline Analysis in Intel Advisor 2017: youtube how-to video". YouTube .
  9. ↑ "Intel Advisor Roofline step-by-step Tutorial".
  10. ↑ "Using Roofline Model and Intel Advisor, presented by Sam Williams, Roofline performance model author".
  11. ↑ "Case Study: SimYog Improves a Simulation Tool Performance by 2x with..." Intel. Retrieved 2021-01-07.
  12. ↑ "Memory-Level Roofline Model with Intel® Advisor". Intel. Retrieved 2021-01-05.
  13. ↑ "CPU / Memory Roofline Insights Perspective". Intel. Retrieved 2021-01-05.
  14. ↑ "GPU Roofline Insights Perspective". Intel. Retrieved 2021-01-05.
  15. ↑ "Integer Roofline Modeling in Intel® Advisor". Intel. Retrieved 2021-01-05.
  16. ↑ "How to model suitability using Advisor XE 2015?".
  17. ↑ "Offload Modeling Resources for Intel® Advisor Users". Intel. Retrieved 2021-01-05.
  18. ↑ "Identify Code Regions to Offload to GPU and Visualize GPU Usage (Beta)". Intel. Retrieved 2021-01-05.
  19. ↑ "Offload Modeling Perspective". Intel. Retrieved 2021-01-05.
  20. ↑ "Schlumberger* - Parallelize Oil and Gas software with Intel Software products" (PDF).
  21. ↑ ""Leading design" company Advisor XE case study" (PDF).
  22. ↑ "Design Code for Parallelism and Offloading with Intel® Advisor".
  23. ↑ "Computer-Aided Formulation case study: getting helping hand from the Vectorization Advisor".
  24. ↑ "Pexip Speeds Enterprise-Grade Videoconferencing" (PDF).
  25. ↑ "Supercomputing'2012 HPC educator with Slippery Rock University".
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