high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

Zachary DeVito, Pat Hanrahan

View

Download (PDF)

Source codes

Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Package, Partial differential equations, PDEs, Performance, Programming Languages, Tesla C2050

October 19, 2011 by hgpu

10×10: A General-purpose Architectural Approach to Heterogeneity and Energy Efficiency

Andrew A. Chien, Allan Snavely, Mark Gahagan

View

Download (PDF)

Tags: Clustering, Computer science, Heterogeneous systems, Optimization

October 19, 2011 by hgpu

Heterogeneous Accelerated Bioinformatics-Perspectives for Cancer Research

Eric A. Stahlberg, T. Steinke, M. C. Smith, S. Chandrasekaran, B. Chapman

View

Download (PDF)

Tags: Bioinformatics, Biology, CUDA, FPGA, Heterogeneous systems, Medicine, nVidia, OpenCL, Review

October 19, 2011 by hgpu

The GPU Computing Revolution: From Multi-Core CPUs To Many-Core Graphics Processors

Simon McIntosh-Smith

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Review, Tesla C2050

October 19, 2011 by hgpu

Fast N-body Simulations on GPUs

Rio Yokota, Lorena A. Barba

View

Download (PDF)

Tags: Algorithms, Astrophysics, Computer science, CUDA, Fast multipole method, Heterogeneous systems, Mathematical Software, N-body simulation, Numerical Analysis, nVidia, Tesla C2050, Tesla M2050

October 18, 2011 by hgpu

Magneto-hydrodynamics simulation in astrophysics

Bijia Pang

View

Download (PDF)

Tags: Algorithms, Astrophysics, CUDA, Fortran, Heterogeneous systems, Magnetohydrodynamics, nVidia, OpenCL, Physics, Tesla C1060, Tesla C2050, Thesis

October 17, 2011 by hgpu

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

Eva Burrows

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 250, Programming techniques, Sorting, Thesis

October 17, 2011 by hgpu

GPU-to-CPU callbacks

Jeff A. Stuart, Michael Cox, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

October 16, 2011 by hgpu

Skeleton Programming for Heterogeneous GPU-based Systems

Usman Dastgeer

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C1060, Thesis

October 16, 2011 by hgpu

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Shuai Che, Jeremy W. Sheaffer, Kevin Skadron

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Optimization, Performance

October 15, 2011 by hgpu

Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems

George Teodoro, Tahsin M. Kurc, Tony Pan, Lee Cooper, Jun Kong, Patrick Widener, Joel H. Saltz

View

Download (PDF)

Tags: CUDA, Heterogeneous systems, Image processing, nVidia, Tesla M2070

October 14, 2011 by hgpu

OptiML: An implicitly parallel domain-specific language for machine learning

Arvind K. Sujeeth, HyoukJoong Lee, Kevin J. Brown, Tiark Rompf, Hassan Chafi, Michael Wu, Anand R. Atreya, Martin Odersky, Kunle Olukotun

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, OpenCL, Optimization, Tesla C2050

October 14, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Designing the Language Liszt for Building Portable Mesh-based PDE Solvers

10×10: A General-purpose Architectural Approach to Heterogeneity and Energy Efficiency

Heterogeneous Accelerated Bioinformatics-Perspectives for Cancer Research

The GPU Computing Revolution: From Multi-Core CPUs To Many-Core Graphics Processors

Fast N-body Simulations on GPUs

Magneto-hydrodynamics simulation in astrophysics

Programming with Explicit Dependencies. A Framework for Portable Parallel Programming

GPU-to-CPU callbacks

Skeleton Programming for Heterogeneous GPU-based Systems

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems

Accelerating Large Scale Image Analyses on Parallel CPU-GPU Equipped Systems

OptiML: An implicitly parallel domain-specific language for machine learning

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

Most viewed papers (last 30 days)