high performance computing on graphics processing units: hgpu.org

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

Masato Mimura, Shinsuke Sakai, Tatsuya Kawahara

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Speech recognition

July 29, 2015 by hgpu

Performance Analysis of a Particle-in-Cell Plasma Physics Code on Homogeneous and Heterogeneous HPC Systems

Xavier Saez, Alejandro Soba, Edilberto Sanchez, Mervi Mantsinen, Jose M. Cela

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Tags: Algorithms, ARM, Heterogeneous systems, nVidia, OpenCL, Particle-in-cell methods, Physics, Plasma physics, Tesla M2090

July 29, 2015 by hgpu

Sound Synthesis Using Physical Modeling on Heterogeneous Computing Platforms

M. Pluta, B. Borkowski, I. Czajka, K. Suder-Debska

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Tags: Acoustics, Algorithms, Computer science, Finite difference, Heterogeneous systems, nVidia, nVidia GeForce GT 750 M, OpenCL

July 29, 2015 by hgpu

Prospects of GPGPU in the Auger Offline Software Framework

Tobias Winchen, Marvin Gottowik, Julian Rautenberg

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 750 Ti, Tesla M2090

July 29, 2015 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

Dense Symmetric Indefinite Factorization on GPU Accelerated Architectures

Face Search at Scale: 80 Million Gallery

Real-Time Scheduling for GPUs with Applications in Advanced Automotive Systems

Multi-GPU Parallel Computing and Task Scheduling under Virtualization

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Parallel Surface Reconstruction on GPU

Investigating SRAM PUFs in large CPUs and GPUs

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

Performance Analysis of a Particle-in-Cell Plasma Physics Code on Homogeneous and Heterogeneous HPC Systems

Sound Synthesis Using Physical Modeling on Heterogeneous Computing Platforms

Prospects of GPGPU in the Auger Offline Software Framework

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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