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Efficient code generation for hardware accelerators by refining partially specified implementation

Ulysse Beaugnon

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Tags: Algorithms, Code generation, Computer science, CUDA, Linear Algebra, nVidia, Optimization, Package, Thesis

December 13, 2020 by hgpu

Exploring FPGA Optimizations to Compute Sparse Numerical Linear Algebra Kernels

Federico Favaro, Ernesto Dufrechou, Pablo Ezzatti, Juan P. Oliver

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Tags: Computer science, FPGA, Linear Algebra, OpenCL, Optimization, Performance, Sparse

December 6, 2020 by hgpu

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Cade Brown, Ahmad Abdelfattah, Stanimire Tomov, Jack Dongarra

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Tags: AMD, AMD Radeon Instinct MI25, AMD Radeon Instinct Mi50, Benchmarking, Computer science, Heterogeneous systems, HIP, HPC, Linear Algebra, Performance, Portability

August 23, 2020 by hgpu

Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse Linear Algebra Computations

Yuhsiang Mike Tsai, Terry Cojean, Hartwig Anzt

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia A100, Performance, Sparse, Sparse matrix

August 23, 2020 by hgpu

Heterogeneous parallel computing for image registration and linear algebra applications

Orestis Zachariadis

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Tags: CUDA, Heterogeneous systems, Image processing, Image registration, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1050, nVidia GeForce RTX 2070, Package, Sparse matrix, Thesis

August 9, 2020 by hgpu

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080, Package, Sparse matrix

July 12, 2020 by hgpu

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Hartwig Anzt, Terry Cojean, Goran Flegar, Fritz Göbel, Thomas Grützmacher, Pratik Nayak, Tobias Ribizel, Yuhsiang Mike Tsai, Enrique S. Quintana-Ortí

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Tags: Algorithms, AMD Radeon VII, ATI, Computer science, CUDA, HIP, Linear Algebra, Mathematical Software, nVidia, Package, Sparse, Sparse matrix, Tesla V100

July 5, 2020 by hgpu

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

Yuhsiang M. Tsai, Terry Cojean, Tobias Ribizel, Hartwig Anzt

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Tags: AMD Radeon VII, ATI, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Package

June 28, 2020 by hgpu

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Mohammadreza Soltaniyeh, Richard P. Martin, Santosh Nagarakatte

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Tags: Computer science, Factorization, FPGA, HLS, Linear Algebra, Matrix multiplication, OpenCL, Sparse

May 10, 2020 by hgpu

Design Space Exploration of an OpenCL Based SAXPY Kernel Implementation on FPGAs

Jannatun Naher, Clay Gloster, Shrikant S. Jadhav, Christopher C. Doss

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Tags: Computer science, Design space exploration, FPGA, Linear Algebra, OpenCL

April 19, 2020 by hgpu

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

Jannatun Naher, Clay Gloster, Shrikant S. Jadhav, Christopher C. Doss

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Tags: Computer science, Design space exploration, DSP, FPGA, Linear Algebra, Machine learning, OpenCL, Sparse matrix

April 12, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

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

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

pyATF: The Auto-Tuning Framework (ATF) in Python

pyATF: Constraint-Based Auto-Tuning in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

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high performance computing on graphics processing units: hgpu.org

Efficient code generation for hardware accelerators by refining partially specified implementation

Exploring FPGA Optimizations to Compute Sparse Numerical Linear Algebra Kernels

Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs

Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse Linear Algebra Computations

Heterogeneous parallel computing for image registration and linear algebra applications

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

Synergistic CPU-FPGA Acceleration of Sparse Linear Algebra

Design Space Exploration of an OpenCL Based SAXPY Kernel Implementation on FPGAs

Using Machine Learning to Estimate Utilization and Throughput for OpenCL-Based SpMV Implementation on an FPGA

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Recent source codes

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

pyATF: The Auto-Tuning Framework (ATF) in Python

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

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