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Performance Portable GPU Code Generation for Matrix Multiplication

Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan Black, OpenCL

February 10, 2016 by hgpu

High-Performance Tensor Contractions for GPUs

A. Abdelfattah, M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, Tz. Kolev, I. Masliah, S. Tomov

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Tags: Algorithms, Code generation, Computer science, CUDA, FEM, Finite element method, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Tesla K40

January 29, 2016 by hgpu

GeNN: a code generation framework for accelerated brain simulations

Esin Yavuz, James Turner, Thomas Nowotny

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Tags: Biology, Code generation, CUDA, Neural networks, Neuroscience, Numerical simulation, nVidia, nVidia Quadro K 2000 M, Package, Tesla C2070, Tesla K40

January 29, 2016 by hgpu

A framework for efficient execution on GPU and CPU+GPU systems

Jean-Francois Dollinger

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Thesis

January 19, 2016 by hgpu

GRATER: An Approximation Workflow for Exploiting Data-Level Parallelism in FPGA Acceleration

Atieh Lotfi, Abbas Rahimi, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Rajesh K. Gupta

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, FPGA, OpenCL

December 12, 2015 by hgpu

Compiling and Optimizing Java 8 Programs for GPU Execution

Kazuaki Ishizaki, Akihiro Hayashi, Gita Koblents, Vivek Sarkar

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Tags: Code generation, Compilers, Computer science, CUDA, Java, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

Akihiro Hayashi, Kazuaki Ishizaki, Vivek Sarkar, Gita Koblents

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Tags: Code generation, Compilers, Computer science, CUDA, Java, Machine learning, nVidia, OpenCL, Tesla K40

October 27, 2015 by ahayashi

MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators and Its Application to the Generation of Parametric CUDA Kernels

Changbo Chen, Xiaohui Chen, Abdoul-Kader Keita, Marc Moreno Maza, Ning Xie

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Tags: Code generation, Computer science, CUDA, nVidia, Package

October 18, 2015 by hgpu

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

Rahul Garg

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Tags: AMD Radeon HD 8650, ATI, ATI Radeon HD 7970, Benchmarking, Code generation, Computer science, Intel HD Graphics 4000, Matlab, Matrix multiplication, nVidia, nVidia GeForce GT 650 M, OpenCL, Package, Python, Tesla C2050, Thesis

October 6, 2015 by hgpu

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Dongrui She, Yifan He, Luc Waeijen, Henk Corporaal

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Tags: Code generation, Compilers, Computer science, Embedded high-performance computing, OpenCL

September 24, 2015 by hgpu

Automatic OpenCL code generation for multi-device heterogeneous architectures

Pei Li, Elisabeth Brunet, Francois Trahay, Christian Parrot, Gael Thomas, Raymond Namyst

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Tags: Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro FX 5800, OpenCL

September 19, 2015 by hgpu

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Mehdi Amini

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Tags: ATI, ATI Radeon HD 6970, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 670, OpenCL, Tesla C1060, Tesla C2070, Thesis

August 24, 2015 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

Performance Portable GPU Code Generation for Matrix Multiplication

High-Performance Tensor Contractions for GPUs

GeNN: a code generation framework for accelerated brain simulations

A framework for efficient execution on GPU and CPU+GPU systems

GRATER: An Approximation Workflow for Exploiting Data-Level Parallelism in FPGA Acceleration

Compiling and Optimizing Java 8 Programs for GPU Execution

Machine-Learning-based Performance Heuristics for Runtime CPU/GPU Selection

MetaFork: A Compilation Framework for Concurrency Models Targeting Hardware Accelerators and Its Application to the Generation of Parametric CUDA Kernels

A Toolkit for Building Dynamic Compilers for Array-Based Languages Targeting CPUs and GPUs

A Co-Design Framework with OpenCL Support for Low-Energy Wide SIMD Processor

Automatic OpenCL code generation for multi-device heterogeneous architectures

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

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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