high performance computing on graphics processing units: hgpu.org

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Ebb: A DSL for Physical Simluation on CPUs and GPUs

Gilbert Louis Bernstein, Chinmayee Shah, Crystal Lemire, Zachary DeVito, Matthew Fisher, Philip Levis, Pat Hanrahan

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Tags: Computer science, CUDA, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, OpenGL, Physics

June 26, 2015 by hgpu

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

S. Cuomo, A. Galletti, G. Giunta, L. Marcellino

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Tags: Algorithms, Computer science, CUDA, Differential equations, GPU cluster, Heterogeneous systems, nVidia, nVidia Quadro K5000, Optical flow, Partial differential equations, PDEs

June 24, 2015 by hgpu

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Karthik Narayan, Ali Punjani, Pieter Abbeel

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Tags: Computer science, Computer vision, CUDA, Machine learning, nVidia, Tesla K40, Visualization

June 24, 2015 by hgpu

Toward GPU Accelerated Data Stream Processing

Marcus Pinnecke, David Broneske, Gunter Saake

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Tags: Algorithms, Computer science, Databases, OpenCL

June 24, 2015 by hgpu

Concurrent Task Execution on the Intel Xeon Phi

Yucheng Zhu

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Tags: Bioinformatics, Biology, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Sequence alignment, Smith-Waterman algorithm, Tesla K20, Thesis

June 22, 2015 by hgpu

GPU accelerated spectral finite elements on all-hex meshes

J.-F. Remacle, R. Gandham, T. Warburton

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Tags: Algorithms, Computer science, CUDA, Numerical Analysis, nVidia, nVidia GeForce GTX 980, OpenCL, Spectral elements, Tesla K40

June 22, 2015 by hgpu

Generating Efficient Tensor Contractions for GPUs

Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Boyana Norris

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Tags: Algorithms, Chemistry, Computer science, CUDA, Fluid dynamics, Machine learning, nVidia, OpenACC, Optimization

June 22, 2015 by hgpu

Autotuning Tensor Contraction Computations on GPUs

Axel Rivera, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Thomas Nelson, Boyana Norris

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Tags: Benchmarking, Code generation, Computer science, CUDA, Matrix multiplication, nVidia, OpenACC, Tesla C2050, Tesla K20

June 19, 2015 by hgpu

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Naveen Anand Subramaniam, Omkar Deshmukh, Vennila Megavannan, Dan Negrut

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20, Tesla K40

June 19, 2015 by hgpu

Bulk GCD Computation Using a GPU to Break Weak RSA Keys

Toru Fujita, Koji Nakano, Yasuaki Ito

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX 780 Ti, Security

June 19, 2015 by hgpu

Parallel BTF Compression with Multi-Level Vector Quantization in OpenCL

P. Egert, V. Havran

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Tags: 3D Graphics and Realism, Algorithms, Compression, Computer science, nVidia, nVidia GeForce GTX 780, OpenCL, Thesis, Vector quantization

June 19, 2015 by hgpu

Visualization of OpenCL Application Execution on CPU-GPU Systems

Amir Kavyan Ziabari, Rafael Ubal, Dana Schaa, David Kaeli

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Tags: ARM, ATI, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Visualization

June 19, 2015 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Ebb: A DSL for Physical Simluation on CPUs and GPUs

Toward a Multi-level Parallel Framework on GPU Cluster with PetSC-CUDA for PDE-based Optical Flow Computation

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Toward GPU Accelerated Data Stream Processing

Concurrent Task Execution on the Intel Xeon Phi

GPU accelerated spectral finite elements on all-hex meshes

Generating Efficient Tensor Contractions for GPUs

Autotuning Tensor Contraction Computations on GPUs

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Bulk GCD Computation Using a GPU to Break Weak RSA Keys

Parallel BTF Compression with Multi-Level Vector Quantization in OpenCL

Visualization of OpenCL Application Execution on CPU-GPU Systems

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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