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Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

Aditya Chilukuri, Josh Milthorpe, Beau Johnston

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Tags: Computer science, Heterogeneous systems, Memory, nVidia, OpenCL, Performance, Tesla P100

March 29, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Towards automated kernel selection in machine learning systems: A SYCL case study

John Lawson

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Tags: AMD R9 Nano, ATI, Computer science, Heterogeneous systems, Machine learning, Package, Performance, SYCL

March 22, 2020 by hgpu

Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application

David Goz, Georgios Ieronymakis, Vassilis Papaefstathiou, Nikolaos Dimou, Sara Bertocco, Giuliano Taffoni, Francesco Simula, Antonio Ragagnin, Luca Tornatore, Igor Coretti

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Tags: ARM, Astrophysics, CUDA, FPGA, Heterogeneous systems, HPC, HSL, N-body simulation, nVidia, OpenCL, Performance, Physics, SoC, Tesla V100

March 15, 2020 by hgpu

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Compilers, Computer science, Deep learning, Machine learning, Performance

February 16, 2020 by hgpu

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

Ahmed E. Helal

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Tags: Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance, Tesla M2050, Thesis

February 9, 2020 by hgpu

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

Ji Liu, Abdullah-Al Kafi, Xipeng Shen, Huiyang Zhou

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Tags: Compilers, Computer science, FPGA, nVidia, nVidia GeForce RTX 2080, OpenCL, Performance

February 9, 2020 by hgpu

Automatically Harnessing Sparse Acceleration

Philip Ginsbach, Bruce Collie, Michael F.P. O'Boyle

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Tags: AMD Radeon R7, ATI, Computer science, CUDA, Fortran, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance

January 26, 2020 by hgpu

Static Analysis and Dynamic Adaptation of Parallelism

Pierre Huchant

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Tags: Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla K20, Tesla M2075, Thesis

January 12, 2020 by hgpu

Performance-Oriented Neural Architecture Search

Andrew Anderson, Jing Su, Rozenn Dahyot, David Gregg

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, SoC

January 12, 2020 by hgpu

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu, Ang Li, Catherine Olschanowsky, Kevin Barker

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

January 12, 2020 by hgpu

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Toomas Remmelg

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Tags: AMD Radeon R9 295X2, ARM, ATI, Computer science, nVidia, nVidia GeForce GTX Titan Black, OpenCL, Performance, performance portability, Thesis

December 29, 2019 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Towards automated kernel selection in machine learning systems: A SYCL case study

Performance and energy footprint assessment of FPGAs and GPUs on HPC systems using Astrophysics application

The Deep Learning Compiler: A Comprehensive Survey

Automated Runtime Analysis and Adaptation for Scalable Heterogeneous Computing

MKPipe: A Compiler Framework for Optimizing Multi-Kernel Workloads in OpenCL for FPGA

Automatically Harnessing Sparse Acceleration

Static Analysis and Dynamic Adaptation of Parallelism

Performance-Oriented Neural Architecture Search

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Automatic Performance Optimisation of Parallel Programs for GPUs via Rewrite Rules

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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