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检索条件"任意字段=2021 IEEE/ACM Workshop on Programming Environments for Heterogeneous Computing, PEHC 2021"

共 19 条记录，以下是1-10 订阅

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Proceedings of pehc 2021: workshop on programming environments for heterogeneous computing, Held in conjunction with SC 2021: The International Conference for High Performance computing, Networking, Storage and Analysis

Proceedings of PEHC 2021: Workshop on Programming Environmen...

引用

2021 ieee/acm workshop on programming environments for heterogeneous computing, pehc 2021

ISBN: (纸本)9781728186764

The proceedings contain 6 papers. The topics discussed include: GenMAT: a general-purpose machine learning-driven auto-tuner for heterogeneous platforms;OSCAR parallelizing and power reducing compiler and API for heterogeneous multicores;a python-based high-level programming flow for CPU-FPGA heterogeneous systems;a holistic systems approach to leveraging heterogeneity;survival of the fittest amidst the Cambrian explosion of processor architectures for artificial intelligence;and designing heterogeneous systems: large scale architectural exploration via simulation.

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A Python-based High-Level programming Flow for CPU-FPGA heterogeneous Systems (Invited Paper)

A Python-based High-Level Programming Flow for CPU-FPGA Hete...

引用

workshop on programming environments for heterogeneous computing (pehc)

作者： Huang, Sitao Wu, Kun Chalamalasetti, Sai Rahul El Hajj, Izzat Xu, Cong Faraboschi, Paolo Chen, Deming Univ Illinois Urbana IL 61801 USA Hewlett Packard Labs Palo Alto CA USA Amer Univ Beirut Beirut Lebanon

ISBN: (纸本)9781728186764

The fast-growing complexity of new applications and new use scenarios poses serious challenges for computing systems. heterogeneous systems consist of different types of processors and accelerators, and provide unique combined benefits of hardware acceleration from each individual component. CPU-FPGA heterogeneous systems provide both programmable logic and general-purpose processors, and they have demonstrated great flexibility, performance, and efficiency. heterogeneous systems have been created and deployed in many different applications and scenarios. However, as system complexity and application complexity grow rapidly, programming and optimizing heterogeneous systems require great manual efforts and consume a lot of time. In this work, we propose a Python-based high-level programming framework to simplify programming and optimization of CPU-FPGA heterogeneous systems. The proposed high-level operations isolate underlying hardware details from programmers and provide more optimization opportunities for the compiler.

关键词： FPGA Python heterogeneous systems compiler

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GenMAT: A General-Purpose Machine Learning-Driven Auto-Tuner for heterogeneous Platforms

GenMAT: A General-Purpose Machine Learning-Driven Auto-Tuner...

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workshop on programming environments for heterogeneous computing (pehc)

作者： Zhang, Naifeng Srivastava, Ajitesh Kannan, Rajgopal Prasanna, Viktor K. Univ Southern Calif Los Angeles CA 90089 USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781728186764

As computing platforms evolve with heterogeneous resources, developing optimized code that fully exploits the computing power becomes increasingly challenging. Domain experts need extensive knowledge of computer architecture, compiler optimizations, and parallel computing to understand which implementation will work best for their problem domain and data. Even with considerable time learning, writing, and debugging high-performance code, such optimizations may not generalize to different inputs, applications, or computing platforms. To assist the end-users in optimally deploying workloads on the heterogeneous environment with high productivity, a fundamental problem is to automatically find the best "variant" of an application-the implementation with the optimal configurations on the most suitable hardware resource resulting in the minimum runtime. We propose GenMAT, a portable tool for identifying the best variant of any application specified as a meta-program with exposed tunable parameters on any hardware. GenMAT automatically profiles the application by varying the exposed tunable parameters to generate a small set of profiling data. Then, GenMAT trains a compact machine learning model that is used to quickly predict the runtimes of a large number of possible parameter settings to identify the best variant. We show that the variant selected by GenMAT has a runtime deviation within 3.5% of the true best variant in determining the best linear algebra library for matrix operations. For identifying the best Halide schedule, GenMAT correctly ranks the runtimes of thousands of candidates with an average Spearman's rank correlation coefficient of 0.95.

关键词： automatic performance tuning heterogeneous platforms performance modeling machine learning

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OSCAR Parallelizing and Power Reducing Compiler and API for heterogeneous Multicores (Invited Paper)

OSCAR Parallelizing and Power Reducing Compiler and API for ...

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workshop on programming environments for heterogeneous computing (pehc)

作者： Kasahara, Hironori Kimura, Keiji Kitamura, Toshiaki Mikami, Hiroki Morita, Kazutaka Fujita, Kazuki Yamamoto, Kazuki Kawasumi, Tohma Waseda Univ Shinjuku Ku Tokyo Japan Nippon Telegraph & Tel Corp Minato Ku Tokyo Japan

ISBN: (纸本)9781728186764

heterogeneous computing systems, connecting general-purpose processor cores with accelerators and/or different kinds of general-purpose processor cores, have been widely used for HPC, cloud servers, self-driving vehicles, AI robots, and so on. They are used to obtain high performance and/or low power consumption. This paper introduces the OSCAR (Optimally Scheduled Advanced Multiprocessor) parallelizing compiler and OSCAR APL They allow users to automatically parallelize and power-reduce a C or Fortran program for various heterogeneous computing systems. OSCAR compiler has been developed since 1983, aiming at co-design of multiprocessor architecture and compiler. Currently, it can generate parallel machine codes for any shared memory homogeneous and heterogeneous multicores with or without hardware cache-coherent mechanism if a sequential C or Fortran compiler exists for the target multicore. OSCAR compiler translates a sequential user program written in C or Fortran into a parallelized C or Fortran program with OSCAR API compatible with frequency-voltage control, clock-gating, and power gating directives for each core, memory module, and interconnect defined in OSCAR API. The generated parallel program consists of threads specified by OpenMP "section" directives. The threads can be compiled into machine codes by an OpenMP compiler or a sequential C or Fortran compiler for a target general-purpose processor cores or accelerator cores. The compilation flow and execution and power-reduce performance for scientific and embedded applications and Deep Learning are shown on several heterogeneous systems, such as a heterogeneous multicore processor having eight general-purpose cores and 4 DRPs, or Dynamically Reconfigurable Processors, a heterogeneous multicore on FPGA using NIOS cores, and a new vector accelerator based on the past Japanese supercomputers and a personal vector supercomputer NEC Aurora Tsubasa.

关键词： paralelizing compiler heterogeneous multicore accelerator

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Designing heterogeneous Systems: Large Scale Architectural Exploration Via Simulation

Designing Heterogeneous Systems: Large Scale Architectural E...

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workshop on programming environments for heterogeneous computing (pehc)

作者： Emmot, Darel Menhusen, Ryan Dauwe, Daniel Kukkala, Vipin Kumar Bresniker, Kirk Hewlett Packard Enterprise Hewlett Packard Labs Ft Collins CO 80528 USA Hewlett Packard Enterprise Hewlett Packard Labs Milpitas CA USA

ISBN: (纸本)9781728186764

The end of Dennard's scaling in 2005 and the emerging end of Moore's Law has resulted in a number of heterogeneous design wins, applied to compute (vector processing (GPUs), vector-matrix multiplication, FPGAs, etc.), memory (High Bandwidth Memory (HBM), Fabric Attached Memory (FAM), memory-driven designs, etc.) and interconnects (CXL, Gen-Z, etc.). Designing these heterogeneous systems is becoming increasingly hard due to a plethora of architectural choices. Whole meta-level programming environments are required for designing and architecting heterogeneity of both systems and the applications running on those systems. Hewlett Packard Enterprise (TM) (HPE) has found Sandia's Structural Simulation Toolkit (SST) to be a powerful aid to architectural exploration and validation of applications optimized for use with Fabric Attached Memory (FAM) with near memory compute abilities. Standard SST components have been augmented with plug-ins modeling Cray Slingshot (TM) Network Interface Controller (NIC) and router elements with drivers for OpenSHMEM and OpenFAM APIs. We anticipate future initiatives calling for dramatic improvement across broader HPC application areas to require refined processes in the collaborative invention of new heterogeneous designs. In this article, we present our process of using white-box characterization of applications at a node level to create abstract models and discuss the methodologies that are used to reliably extend simulations to scales of 10's of thousands of nodes to estimate large scale throughput. Our application and API simulation methodology ensures high communication resource utilization with robust, straightforward interfaces conducive to collaborative heterogeneous accelerator integration. Application and system developers are thus enabled to exploit heterogeneity to support higher system throughput.

关键词： large-scale systems heterogeneous HPC systems model abstraction modeling and simulation SST OpenFAM Slingshot white-box characterization

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workshop Organization

Workshop Organization

引用

programming environments for heterogeneous computing (pehc), ieee/acm

Provides a listing of current committee members and society officers.

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Message from the workshop Chairs

Message from the Workshop Chairs

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programming environments for heterogeneous computing (pehc), ieee/acm

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Message from the workshop Chairs

Proceedings of PEHC 2021: Workshop on Programming Environmen...

引用

Proceedings of pehc 2021: workshop on programming environments for heterogeneous computing, Held in conjunction with SC 2021: The International Conference for High Performance computing, Networking, Storage and Analysis 2021年 V页

作者： Milojicic, Dejan Badia, Rosa M Gokhale, Maya Hoefler, Torsten Hwu, Wen-Mei Kasahara, Hironori Hewlett Packard Labs Barcelona Supercomputing Center ETH Zurich Lawrence Livermore Lab University of Illinois at Urbana-Champaign United States Waseda University Japan

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Evaluation of Distributed Tasks in Stencil-based Application on GPUs 6

Evaluation of Distributed Tasks in Stencil-based Application...

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ieee/acm 6th International workshop on Extreme Scale programming Models and Middleware (ESPM2)

作者： Raut, Eric Anderson, Jonathon Araya-Polo, Mauricio Meng, Jie SUNY Stony Brook Stony Brook NY 11794 USA Rice Univ Houston TX 77251 USA TotalEnergies EP Res & Technol USA LLC Houston TX 77002 USA

ISBN: (纸本)9781665411400

In the era of exascale computing, the traditional MPI+X paradigm starts losing its strength in taking advantage of heterogeneous systems. Subsequently, research and development on finding alternative programming models and runtimes have become increasingly popular. This encourages comparison, on competitive grounds, of these emerging parallel programming approaches against the traditional MPI+X paradigm. In this work, an implementation of distributed task-based stencil numerical simulation is compared with a MPI+X implementation of the same application. To be more specific, the Legion task-based parallel programming system is used as an alternative to MPI at out-of-node level, while the underlying CUDA-implemented kernels are kept at node level. Therefore, the comparison is as fair as possible and focused on the distributed aspects of the simulation. Overall, the results show that the task-based approach is on par with the traditional MPI approach in terms of both performance and scalability.

关键词： tasks stencil finite difference wave equation heterogeneous GPU

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Flacc: Towards OpenACC support for Fortran in the LLVM Ecosystem 7

Flacc: Towards OpenACC support for Fortran in the LLVM Ecosy...

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ieee/acm 7th Annual workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC)

作者： Clement, Valentin Vetter, Jeffrey S. Oak Ridge Natl Lab Programming Syst Grp Oak Ridge TN 37830 USA Oak Ridge Natl Lab Adv Comp Syst Res Sect Oak Ridge TN USA

ISBN: (纸本)9781665411349

OpenACC is a directive-based programming model for heterogeneous accelerators initially launched in 2010 to provide a portable solution at a level of abstraction above OpenCL, CUDA, and other lower-level programming models. Various implementations of OpenACC for C, C++, and Fortran exist;however, only one open-source, production implementation of OpenACC for Fortran does exist. Moreover, most contemporary compiler tool chains for heterogeneous computing are based on LLVM. This lack of support poses a serious risk for high-performance computing application developers targeting GPUs and other accelerators, and it limits the ability of the community to experiment with, extend, and contribute to the OpenACC specification and open-source implementation itself. To address this gap, we have designed and begun implementing Flacc: an effort funded by the US Exascale computing Project to develop production OpenACC compiler support for Fortran based on Flang within the LLVM ecosystem. In this paper, we describe the Flacc goals, initial design and prototype, and challenges that we have encountered so far in our prototyping efforts. Flacc is implemented as a MLIR dialect in the Flang Fortran front end in LLVM. The Flacc front end currently supports OpenACC version 3.1, and the Flacc run time is currently under development and relies on contributions from the Clacc project. Current contributions to Flacc are available in the main LLVM repository.(1)

关键词： OpenACC OpenMP LLVM MLIR multicore GPU accelerators compiler

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