high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

CnC-CUDA: declarative programming for GPUs

Max Grossman, Alina Simion Sbirlea, Zoran Budimlic, Vivek Sarkar

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Performance

August 24, 2011 by hgpu

Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system

Linchuan Li, Xingjian Li, Guangming Tan, Mingyu Chen, Peiheng Zhang

Tags: Algorithms, Computer science, Cryo-EM, CUDA, Data parallelism, GPU cluster, Heterogeneous systems, MPI, nVidia, Performance, Tesla C2050

August 24, 2011 by hgpu

An open framework for rapid prototyping of signal processing applications

Maxime Pelcat, Jonathan Piat, Matthieu Wipliez, Slaheddine Aridhi, and Jean-Francois Nezan

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Tags: Algorithms, Graph theory, Signal processing

August 24, 2011 by hgpu

Precise dynamic analysis for slack elasticity: adding buffering without adding bugs

Sarvani Vakkalanka, Anh Vo, Ganesh Gopalakrishnan, Robert M. Kirby

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Tags: Algorithms, Computer science, MPI, Network communications

August 24, 2011 by hgpu

JCudaMP: OpenMP/Java on CUDA

Georg Dotzler, Ronald Veldema, Michael Klemm

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Tags: Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce GTX 280, OpenMP, Programming Languages, Programming techniques

August 23, 2011 by hgpu

Implementing the PGI Accelerator model

Michael Wolfe

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization, Programming Languages, Tesla C1060, Tesla S1070

August 23, 2011 by hgpu

Bounding the effect of partition camping in GPU kernels

Ashwin M. Aji, Mayank Daga, Wu-chun Feng

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Tags: Benchmarking, Computer science, CUDA, Memory, nVidia, nVidia GeForce GTX 280, Performance

August 23, 2011 by hgpu

Fast parallel surface and solid voxelization on GPUs

Michael Schwarz, Hans-Peter Seidel

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480

August 23, 2011 by hgpu

High performance content-based matching using GPUs

Alessandro Margara, Gianpaolo Cugola

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Source codes

Tags: Algorithms, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 460, Package, Performance, Programming techniques

August 23, 2011 by hgpu

Efficient implementation of GPGPU synchronization primitives on CPUs

Jayanth Gummaraju, Ben Sander, Laurent Morichetti, Benedict Gaster, Lee Howes

Tags: Computer science, OpenCL, Performance, Programming techniques

August 23, 2011 by hgpu

Performance Modelling and Traffic Characterisation of Optical Networks

Harry Mouchos, Athanasios Tsokanos and Demetres D. Kouvatsos

Tags: Computer science, CUDA, Network communications, Networks, nVidia

August 23, 2011 by hgpu

Auto-tuning 3-D FFT library for CUDA GPUs

Akira Nukada, Satoshi Matsuoka

Tags: Algorithms, Computer science, CUDA, FFT, Mathematical Software, nVidia, Performance

August 22, 2011 by hgpu

Tutoring LLM into a Better CUDA Optimizer

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

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

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

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high performance computing on graphics processing units: hgpu.org

Programming

CnC-CUDA: declarative programming for GPUs

Experience of parallelizing cryo-EM 3D reconstruction on a CPU-GPU heterogeneous system

An open framework for rapid prototyping of signal processing applications

Precise dynamic analysis for slack elasticity: adding buffering without adding bugs

JCudaMP: OpenMP/Java on CUDA

Implementing the PGI Accelerator model

Bounding the effect of partition camping in GPU kernels

Fast parallel surface and solid voxelization on GPUs

High performance content-based matching using GPUs

Efficient implementation of GPGPU synchronization primitives on CPUs

Performance Modelling and Traffic Characterisation of Optical Networks

Auto-tuning 3-D FFT library for CUDA GPUs

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Tutoring LLM into a Better CUDA Optimizer

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

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