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OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Seyong Lee, Rudolf Eigenmann

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, OpenMP, Performance, Programming techniques

March 26, 2012 by hgpu

Expressive Array Constructs in an Embedded GPU Kernel Programming Language

Koen Claessen, Mary Sheeran, Bo Joel Svensson

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Tags: Algorithms, Code generation, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sorting

March 13, 2012 by hgpu

Scalable framework for mapping streaming applications onto multi-GPU systems

Huynh Phung Huynh, Andrei Hagiescu, Weng-Fai Wong, Rick Siow Mong Goh

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Tags: Algorithms, Code generation, Computer science, CUDA, GPU cluster, nVidia, Tesla C2070

March 8, 2012 by hgpu

Paragon: Collaborative Speculative Loop Execution on GPU and CPU

Mehrzad Samadi, Amir Hormati, Janghaeng Lee, Scott Mahlke

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 560, Optimization, Programming techniques

March 8, 2012 by hgpu

A Restructuring Algorithm for CUDA

M. A. Al-Mouhamed, A. H. Khan

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia

February 29, 2012 by hgpu

An Energy Consumption Model for GPU Computing at Instruction Level

Haifeng Wang, Qingkui Chen

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Tags: Code generation, Computer science, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, PTX

February 27, 2012 by hgpu

Introducing ‘Bones’: A Parallelizing Source-to-Source Compiler Based on Algorithmic Skeletons

Cedric Nugteren, Henk Corporaal

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Tags: Algorithms, ATI, ATI Radeon HD 5850, Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 470, OpenCL

February 20, 2012 by hgpu

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Cedric Nugteren, Henk Corporaal, Bart Mesman

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Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce GTX 470, Optimization

February 17, 2012 by hgpu

Comparing Many-Core Accelerator Frameworks

G. Haase, A. Kucher

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Optimization

February 15, 2012 by hgpu

Symbolic Testing of OpenCL Code

Peter Collingbourne, Cristian Cadar, Paul H. J. Kelly

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Tags: Benchmarking, Code generation, Computer science, nVidia, OpenCL, Package, PTX, Tesla C1060

February 8, 2012 by hgpu

Decompilation of LLVM IR

Simon Moll

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Tags: Code generation, Compilers, Computer science, GLSL, HLSL, Java, nVidia, nVidia GeForce 9600 GT, OpenCL, Optimization, Thesis

January 31, 2012 by hgpu

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Johan Enmyren, Usman Dastgeer, Christoph W. Kessler

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Tags: Code generation, Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce GTS 250, OpenCL, Performance, Tesla C1060

January 30, 2012 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

OpenMPC: Extended OpenMP for Efficient Programming and Tuning on GPUs

Expressive Array Constructs in an Embedded GPU Kernel Programming Language

Scalable framework for mapping streaming applications onto multi-GPU systems

Paragon: Collaborative Speculative Loop Execution on GPU and CPU

A Restructuring Algorithm for CUDA

An Energy Consumption Model for GPU Computing at Instruction Level

Introducing ‘Bones’: A Parallelizing Source-to-Source Compiler Based on Algorithmic Skeletons

Skeleton-based Automatic Parallelization of Image Processing Algorithms for GPUs

Comparing Many-Core Accelerator Frameworks

Symbolic Testing of OpenCL Code

Decompilation of LLVM IR

Towards a Tunable Multi-Backend Skeleton Programming Framework for Multi-GPU Systems

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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