high performance computing on graphics processing units: hgpu.org

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Fast Neural Network Training on General Purpose Computers

Harshit Kharbanda, Roy H. Campbell

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia Quadro FX 1800

December 14, 2011 by hgpu

GPU Programming in a High Level Language: Compiling X10 to CUDA

Dave Cunningham, Rajesh Bordawekar, Vijay Saraswat

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia Quadro FX 3700 M, OpenCL, Tesla C1060, Tesla S1070

December 12, 2011 by hgpu

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Hemant Shukla, Hsi-Yu Schive, Tak-Pong Woo, Tzihong Chiueh

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Tags: Algorithms, Astrophysics, Benchmarking, CUDA, Differential equations, nVidia, Partial differential equations, PDEs, Physics, Rendering, Tesla C2050

December 10, 2011 by hgpu

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Deepak Mathews Panickal

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Tags: ATI, ATI Radeon HD 5470, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Tesla C2070, Thesis

December 9, 2011 by hgpu

Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

Markus Geveler, Dirk Ribbrock, Dominik Goddeke, Peter Zajac, Stefan Turek

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Tags: Benchmarking, CUDA, Differential equations, FEM, Finite element method, Fluid dynamics, nVidia, nVidia GeForce GTX 285, Partial differential equations, PDEs, Sparse matrix

December 7, 2011 by hgpu

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

Alecio Pedro Delazari Binotto

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, OpenCL, Task scheduling, Thesis

December 5, 2011 by hgpu

GROPHECY: GPU performance projection from CPU code skeletons

Jiayuan Meng, Vitali A. Morozov, Kalyan Kumaran, Venkatram Vishwanath, Thomas D. Uram

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Tags: Analytical model, Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 5600, Performance, Tesla C1060

December 5, 2011 by hgpu

GPU-Based Parallel Multi-objective Particle Swarm Optimization

You Zhou, Ying Tan

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce 9800 GT, Optimization

December 4, 2011 by hgpu

Comparison of CPML Implementations for the GPU-Accelerated FDTD Solver

J.I. Toivanen, T.P. Stefanski, N. Kuster, N. Chavannes

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Tags: Benchmarking, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, nVidia, Tesla C1060

December 3, 2011 by hgpu

On the design of architecture-aware algorithms for emerging applications

Seunghwa Kang

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Tags: Algorithms, Benchmarking, Biology, Cell processor, Computational biology, CUDA, Image processing, MapReduce, nVidia, Package, Tesla C1060, Thesis

December 2, 2011 by hgpu

An error correction solver for linear systems: Evaluation of mixed precision implementations

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker

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Tags: Benchmarking, Computer science, CUDA, Mixed precision, nVidia, Tesla S1070

December 2, 2011 by hgpu

GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating

Hao Wu, Junbo Xu, Shengfei Zhang, Hao Wen

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Tags: Algorithms, Benchmarking, Chemistry, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 285, Optimization

November 30, 2011 by hgpu

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

Fast Neural Network Training on General Purpose Computers

GPU Programming in a High Level Language: Compiling X10 to CUDA

Multi-Science Applications with Single Codebase – GAMER – for Massively Parallel Architectures

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Towards a complete FEM-based simulation toolkit on GPUs: Geometric Multigrid solvers

A dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

GROPHECY: GPU performance projection from CPU code skeletons

GPU-Based Parallel Multi-objective Particle Swarm Optimization

Comparison of CPML Implementations for the GPU-Accelerated FDTD Solver

On the design of architecture-aware algorithms for emerging applications

An error correction solver for linear systems: Evaluation of mixed precision implementations

GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating

Recent source codes

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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