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Other Apis What’S Wrong with Openmp?
Threaded Programming Other APIs What’s wrong with OpenMP? • OpenMP is designed for programs where you want a fixed number of threads, and you always want the threads to be consuming CPU cycles. – cannot arbitrarily start/stop threads – cannot put threads to sleep and wake them up later • OpenMP is good for programs where each thread is doing (more-or-less) the same thing. • Although OpenMP supports C++, it’s not especially OO friendly – though it is gradually getting better. • OpenMP doesn’t support other popular base languages – e.g. Java, Python What’s wrong with OpenMP? (cont.) Can do this Can do this Can’t do this Threaded programming APIs • Essential features – a way to create threads – a way to wait for a thread to finish its work – a mechanism to support thread private data – some basic synchronisation methods – at least a mutex lock, or atomic operations • Optional features – support for tasks – more synchronisation methods – e.g. condition variables, barriers,... – higher levels of abstraction – e.g. parallel loops, reductions What are the alternatives? • POSIX threads • C++ threads • Intel TBB • Cilk • OpenCL • Java (not an exhaustive list!) POSIX threads • POSIX threads (or Pthreads) is a standard library for shared memory programming without directives. – Part of the ANSI/IEEE 1003.1 standard (1996) • Interface is a C library – no standard Fortran interface – can be used with C++, but not OO friendly • Widely available – even for Windows – typically installed as part of OS – code is pretty portable • Lots of low-level control over behaviour of threads • Lacks a proper memory consistency model Thread forking #include <pthread.h> int pthread_create( pthread_t *thread, const pthread_attr_t *attr, void*(*start_routine, void*), void *arg) • Creates a new thread: – first argument returns a pointer to a thread descriptor. -
Intel® Parallel Studio
Intel® Parallel Studio Product Brief Parallelism for Your Development Lifecycle Intel® Parallel Studio Intel® Parallel Studio brings comprehensive parallelism to C/C++ Microsoft Visual Studio* application development. Parallel Studio was created in direct response to the concerns of software industry leaders and developers. From the way the products work together to support the development lifecycle to their unique feature sets, parallelism is now easier and more viable than ever before. The tools are designed so those new to parallelism can learn as they go, and experienced parallel programmers can work more efficiently and with more confidence. Parallel Studio is interoperable with common parallel programming libraries and API standards, such as Intel® Threading Building Blocks (Intel® TBB) and OpenMP*, and provides an immediate opportunity to realize the benefits of multicore platforms. “Intel® Parallel Studio makes the new Envivio 4Caster Series Transcoder’s development faster and more efficient. The tools included in Intel Parallel Studio, such as Intel® Parallel Inspector, Intel® Parallel Amplifier, and Intel® Parallel Composer (which consists of the Intel® C++ Compiler, Intel® IPP, and Intel® TBB) shortens our overall software development time by increasing the code’s reliability and its performance in a multicore multithreaded environment. At the qualification stage, the number of dysfunctions is reduced due to a safer implementation, and the bug tracking becomes easier too. Intel Parallel Studio globally speeds up our software products’ time-to-market”. Eric Rosier V.P. Engineering Envivio Intel® Parallel Studio Tools c. How can you actually boost performance of your threaded application on multicore processors and make the performance scale with additional cores? Intel® Parallel Studio Workflow The workflow diagram below depicts a typical usage model across all Intel Parallel Studio Addresses the Issues Listed Above. -
Michael Steyer Technical Consulting Engineer Intel Architecture, Graphics & Software Analysis Tools
Michael Steyer Technical Consulting Engineer Intel Architecture, Graphics & Software Analysis Tools Optimization Notice Copyright © 2020, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Aspects of HPC/Throughput Application Performance What are the Aspects of Performance Intel Hardware Features Multi-core Intel® Omni Intel® Optane™ Intel® Advanced Intel® Path HBM DC persistent Vector Xeon® Extensions 512 Architecture memory (Intel® AVX-512) processor Distributed memory Memory I/O Threading CPU Core Message size False Sharing File I/O Threaded/serial ratio uArch issues (IPC) Rank placement Access with strides I/O latency Thread Imbalance Vectorization Rank Imbalance Latency I/O waits RTL overhead FPU usage efficiency RTL Overhead Bandwidth System-wide I/O (scheduling, forking) Network Bandwidth NUMA Synchronization Cluster Node Core Optimization Notice Copyright © 2020, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. IntelWhat Parallel are the Studio Aspects Tools covering of Performance the Aspects Intel Hardware Features Multi-core Intel® Intel® Omni Intel® Optane™ Advanced Intel®Path DC persistent Intel® Vector HBM Extensions Architectur Intel® VTune™memory AmplifierXeon® processor 512 (Intel® Tracee Intel®AVX-512) DistributedAnalyzer memory Memory I/O Threading AdvisorCPU Core Messageand size False Sharing File I/O Threaded/serial ratio uArch issues (IPC) Rank placement Access with strides I/O latency Thread Imbalance Vectorization RankCollector Imbalance Latency I/O waits RTL overhead FPU usage efficiency RTL Overhead Bandwidth System-wide I/O (scheduling, forking) Network Bandwidth NUMA Synchronization Cluster Node Core Optimization Notice Copyright © 2020, Intel Corporation. All rights reserved. -
Concurrent Cilk: Lazy Promotion from Tasks to Threads in C/C++
Concurrent Cilk: Lazy Promotion from Tasks to Threads in C/C++ Christopher S. Zakian, Timothy A. K. Zakian Abhishek Kulkarni, Buddhika Chamith, and Ryan R. Newton Indiana University - Bloomington, fczakian, tzakian, adkulkar, budkahaw, [email protected] Abstract. Library and language support for scheduling non-blocking tasks has greatly improved, as have lightweight (user) threading packages. How- ever, there is a significant gap between the two developments. In previous work|and in today's software packages|lightweight thread creation incurs much larger overheads than tasking libraries, even on tasks that end up never blocking. This limitation can be removed. To that end, we describe an extension to the Intel Cilk Plus runtime system, Concurrent Cilk, where tasks are lazily promoted to threads. Concurrent Cilk removes the overhead of thread creation on threads which end up calling no blocking operations, and is the first system to do so for C/C++ with legacy support (standard calling conventions and stack representations). We demonstrate that Concurrent Cilk adds negligible overhead to existing Cilk programs, while its promoted threads remain more efficient than OS threads in terms of context-switch overhead and blocking communication. Further, it enables development of blocking data structures that create non-fork-join dependence graphs|which can expose more parallelism, and better supports data-driven computations waiting on results from remote devices. 1 Introduction Both task-parallelism [1, 11, 13, 15] and lightweight threading [20] libraries have become popular for different kinds of applications. The key difference between a task and a thread is that threads may block|for example when performing IO|and then resume again. -
Accelerate AI, HPC, Enterprise & Cloud Applications
Accelerate AI, HPC, Enterprise & Cloud Applications April 2019 @ CiTIUS: Centro Singular de Investigación en Tecnoloxías da Información Intel Computing Performance and Software Products (CPDP) Edmund Preiss Agenda • Intel Software Development Tools • Intel optimized AI Solutions Optimization Notice Copyright © 2018, Intel Corporation. All rights reserved. 3 *Other names and brands may be claimed as the property of others. Intel® Software Developer Tools & SDKs Intel® Parallel Studio XE Intel® System Studio Comprehensive Enterprise , HPC Embedded Tools Suite Tools suite Comprehensive, all-in-one, cross-platform Shared and distributed memory system & IoT development tool suite systems Simplifies system bring-up, boosts Code creation and versatile SW performance and power efficiency, Analysis Tools strengthens system reliability Intel® Media Server Studio OpenVINO™ Media Encode/Decode Tools Machine Learning / Deep Learning Inference Media SDK Computer Vision SDK Graphics Perf Analyzer Deep Learning (DL) Deployment Toolkit Computer Vision SDK Deep Learning Algorithms Open CL SDK Optimized DL Frameworks Context SDK Optimization Notice Copyright © 2018, Intel Corporation. All rights reserved. INTEL CONFIDENTIAL 11 *Other names and brands may be claimed as the property of others. What’s Inside Intel® Parallel Studio XE Comprehensive Software Development Tool Suite Cluster Edition Composer Edition Professional Edition BUILD ANALYZE SCALE Compilers & Libraries Analysis Tools Cluster Tools C / C++ Compiler Intel® Math Kernel Library Intel® VTune™ -
Intel® Software Products Highlights and Best Practices
Intel® Software Products Highlights and Best Practices Edmund Preiss Business Development Manager Entdecken Sie weitere interessante Artikel und News zum Thema auf all-electronics.de! Hier klicken & informieren! Agenda • Key enhancements and highlights since ISTEP’11 • Industry segments using Intel® Software Development Products • Customer Demo and Best Practices Copyright© 2012, Intel Corporation. All rights reserved. 2 *Other brands and names are the property of their respective owners. Key enhancements & highlights since ISTEP’11 3 All in One -- Intel® Cluster Studio XE 2012 Analysis & Correctness Tools Shared & Distributed Memory Application Development Intel Cluster Studio XE supports: -Shared Memory Processing MPI Libraries & Tools -Distributed Memory Processing Compilers & Libraries Programming Models -Hybrid Processing Copyright© 2012, Intel Corporation. All rights reserved. *Other brands and names are the property of their respective owners. Intel® VTune™ Amplifier XE New VTune Amplifier XE features very well received by Software Developers Key reasons : • More intuitive – Improved GUI points to application inefficiencies • Preconfigured & customizable analysis profiles • Timeline View highlights concurrency issues • New Event/PC counter ratio analysis concept easy to grasp Copyright© 2012, Intel Corporation. All rights reserved. *Other brands and names are the property of their respective owners. Intel® VTune™ Amplifier XE The Old Way versus The New Way The Old Way: To see if there is an issue with branch misprediction, multiply event value (86,400,000) by 14 cycles, then divide by CPU_CLK_UNHALTED.THREAD (5,214,000,000). Then compare the resulting value to a threshold. If it is too high, investigate. The New Way: Look at the Branch Mispredict metric, and see if any cells are pink. -
Parallelism in Cilk Plus
Cilk Plus: Language Support for Thread and Vector Parallelism Arch D. Robison Intel Sr. Principal Engineer Outline Motivation for Intel® Cilk Plus SIMD notations Fork-Join notations Karatsuba multiplication example GCC branch Copyright© 2012, Intel Corporation. All rights reserved. 2 *Other brands and names are the property of their respective owners. Multi-Threading and Vectorization are Essential to Performance Latest Intel® Xeon® chip: 8 cores 2 independent threads per core 8-lane (single prec.) vector unit per thread = 128-fold potential for single socket Intel® Many Integrated Core Architecture >50 cores (KNC) ? independent threads per core 16-lane (single prec.) vector unit per thread = parallel heaven Copyright© 2012, Intel Corporation. All rights reserved. 3 *Other brands and names are the property of their respective owners. Importance of Abstraction Software outlives hardware. Recompiling is easier than rewriting. Coding too closely to hardware du jour makes moving to new hardware expensive. C++ philosophy: abstraction with minimal penalty Do not expect compiler to be clever. But let it do tedious bookkeeping. Copyright© 2012, Intel Corporation. All rights reserved. 4 *Other brands and names are the property of their respective owners. “Three Layer Cake” Abstraction Message Passing exploit multiple nodes Fork-Join exploit multiple cores exploit parallelism at multiple algorithmic levels SIMD exploit vector hardware Copyright© 2012, Intel Corporation. All rights reserved. 5 *Other brands and names are the property of their respective owners. Composition Message Driven compose via send/receive Fork-Join compose via call/return SIMD compose sequentially Copyright© 2012, Intel Corporation. All rights reserved. 6 *Other brands and names are the property of their respective owners. -
5. Oneapi in Der Praxis
Praxis Gekonntes Zusammenspiel von HPC und AI im Alltag der Software-Entwickler oneAPI in der Praxis von Harald Odendahl Aus Parallel Studio XE wird oneAPI Toolkit: Software-Entwickler können ab Dezember 2020 auf die neue Generation der Programmier-Tools von Intel zugreifen. Die klassischen Intel-Tools sowie der professionelle Kunden-Support bleiben erhalten. Der Praxiseinsatz wird jedoch mächtig erweitert. Mit oneAPI bekräftigt Intel seinen Umschwung zu einer „Software First“-Strategie, um die Herausforderungen und Bedürfnisse der Programmierer bei der Entwicklung hochperformanter wissenschaftlicher und technischer Applikationen sowie KI-Anwendungen zu priorisieren. Das Design neuer Architekturen und Komponenten wie CPUs oder GPUs soll nun gleichzeitig und im Einklang mit den Innovationen bei Programmiermodellen und Entwicklertools erfolgen. So kündigte Intel auf der (virtuellen) Konferenz SuperComputing 2020 an, dass das von Native-Code-Entwicklern geschätzte und intensiv genutzte Intel Parallel Studio XE Toolkit ab Anfang 2021 von den neuen Toolkits der Produktfamilie oneAPI abgelöst werden wird. Intel will auf diesem Weg auch seinen Entwicklerkreis erweitern und bietet deshalb eine Reihe separater Toolkits für verschiedene Anwendungsgebiete. Historisch gesehen sind die klassischen Intel-Entwicklertools die Compiler für C/C++ sowie Fortran, die Analyse-Tools für Code- Optimierung sowie die bestbewährten Bibliotheken, wie zum Beispiel MKL oder IPP. Alle Komponenten werden in den neuen Toolkits vollständig übernommen. Die Leistungen für professionelle Nutzer, wie direkter Support von Intel-Ingenieuren und die Softwarewartung, bleiben bei weitgehend gleicher Preisstruktur erhalten. Erweiterte Tool-Palette für neue Nutzerkreise Im Packaging wird es jedoch eine Reihe von Änderungen geben. Zunächst führt Intel ein sogenanntes „oneAPI Base Toolkit“ ein, das einerseits die wesentlichen Komponenten für die architekturübergreifende Entwicklung zur Verfügung stellt und andererseits als Grundlage für weitere Toolkits dient. -
Intel® Parallel Studio Xe 2017 Runtime
Intel® Parallel StudIo Xe 2017 runtIme Release Notes 26 July 2016 Contents 1 Introduction ................................................................................................................................................... 1 1.1 What Every User Should Know About This Release ..................................................................... 1 2 Product Contents ......................................................................................................................................... 2 3 System Requirements ................................................................................................................................ 3 3.1 Processor Requirements........................................................................................................................... 3 3.2 Disk Space Requirements ......................................................................................................................... 3 3.3 Operating System Requirements .......................................................................................................... 3 3.4 Memory Requirements .............................................................................................................................. 3 3.5 Additional Software Requirements ...................................................................................................... 3 4 Issues and Limitations .............................................................................................................................. -
A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization
TREES: A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization Blake A. Hechtman, Andrew D. Hilton, and Daniel J. Sorin Department of Electrical and Computer Engineering Duke University Abstract —We have developed a task-parallel runtime targeting CPUs are a poor fit for GPUs. To understand system, called TREES, that is designed for high why this mismatch exists, we must first understand the performance on CPU/GPU platforms. On platforms performance of an idealized task-parallel application with multiple CPUs, Cilk’s “work-first” principle (with no runtime) and then how the runtime’s overhead underlies how task-parallel applications can achieve affects it. The performance of a task-parallel application performance, but work-first is a poor fit for GPUs. We is a function of two characteristics: its total amount of build upon work-first to create the “work-together” work to be performed (T1, the time to execute on 1 principle that addresses the specific strengths and processor) and its critical path (T∞, the time to execute weaknesses of GPUs. The work-together principle on an infinite number of processors). Prior work has extends work-first by stating that (a) the overhead on shown that the runtime of a system with P processors, the critical path should be paid by the entire system at TP, is bounded by = ( ) + ( ) due to the once and (b) work overheads should be paid co- greedy o ff line scheduler bound [3][10]. operatively. We have implemented the TREES runtime A task-parallel runtime introduces overheads and, for in OpenCL, and we experimentally evaluate TREES purposes of performance analysis, we distinguish applications on a CPU/GPU platform. -
Intel Threading Building Blocks
Praise for Intel Threading Building Blocks “The Age of Serial Computing is over. With the advent of multi-core processors, parallel- computing technology that was once relegated to universities and research labs is now emerging as mainstream. Intel Threading Building Blocks updates and greatly expands the ‘work-stealing’ technology pioneered by the MIT Cilk system of 15 years ago, providing a modern industrial-strength C++ library for concurrent programming. “Not only does this book offer an excellent introduction to the library, it furnishes novices and experts alike with a clear and accessible discussion of the complexities of concurrency.” — Charles E. Leiserson, MIT Computer Science and Artificial Intelligence Laboratory “We used to say make it right, then make it fast. We can’t do that anymore. TBB lets us design for correctness and speed up front for Maya. This book shows you how to extract the most benefit from using TBB in your code.” — Martin Watt, Senior Software Engineer, Autodesk “TBB promises to change how parallel programming is done in C++. This book will be extremely useful to any C++ programmer. With this book, James achieves two important goals: • Presents an excellent introduction to parallel programming, illustrating the most com- mon parallel programming patterns and the forces governing their use. • Documents the Threading Building Blocks C++ library—a library that provides generic algorithms for these patterns. “TBB incorporates many of the best ideas that researchers in object-oriented parallel computing developed in the last two decades.” — Marc Snir, Head of the Computer Science Department, University of Illinois at Urbana-Champaign “This book was my first introduction to Intel Threading Building Blocks. -
An Overview of Parallel Ccomputing
An Overview of Parallel Ccomputing Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS2101 Plan 1 Hardware 2 Types of Parallelism 3 Concurrency Platforms: Three Examples Cilk CUDA MPI Hardware Plan 1 Hardware 2 Types of Parallelism 3 Concurrency Platforms: Three Examples Cilk CUDA MPI Hardware von Neumann Architecture In 1945, the Hungarian mathematician John von Neumann proposed the above organization for hardware computers. The Control Unit fetches instructions/data from memory, decodes the instructions and then sequentially coordinates operations to accomplish the programmed task. The Arithmetic Unit performs basic arithmetic operation, while Input/Output is the interface to the human operator. Hardware von Neumann Architecture The Pentium Family. Hardware Parallel computer hardware Most computers today (including tablets, smartphones, etc.) are equipped with several processing units (control+arithmetic units). Various characteristics determine the types of computations: shared memory vs distributed memory, single-core processors vs multicore processors, data-centric parallelism vs task-centric parallelism. Historically, shared memory machines have been classified as UMA and NUMA, based upon memory access times. Hardware Uniform memory access (UMA) Identical processors, equal access and access times to memory. In the presence of cache memories, cache coherency is accomplished at the hardware level: if one processor updates a location in shared memory, then all the other processors know about the update. UMA architectures were first represented by Symmetric Multiprocessor (SMP) machines. Multicore processors follow the same architecture and, in addition, integrate the cores onto a single circuit die. Hardware Non-uniform memory access (NUMA) Often made by physically linking two or more SMPs (or multicore processors).