high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Yuechao Lu, Fumihiko Ino, Yasuyuki Matsushita

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Tags: Algorithms, Computer science, Computer vision, CUDA, Linear Algebra, Machine learning, nVidia, Tesla P100

June 25, 2017 by hgpu

Multi-level Parallelism with MPI and OpenACC for CFD Applications

Andrew J. McCall

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Tags: cfd, Fluid dynamics, Fortran, MPI, nVidia, OpenACC, Tesla C2075, Tesla K80, Tesla M2050, Thesis

June 21, 2017 by hgpu

On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization

Jose A. Belloch, Jose M. Badia, Francisco D. Igual, Maximo Cobos, Enrique S. Quintana-Orti

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Tags: Algorithms, ARM, nVidia, nVidia GeForce GTX 1080, OpenCL, Signal processing, Tesla K20

June 21, 2017 by hgpu

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Mohammed Sourouri, Scott B. Baden, Xing Cai

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Tags: Code generation, Compilers, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, OpenMP, Tesla K20

June 21, 2017 by hgpu

Rgtsvm: Support Vector Machines on a GPU in R

Zhong Wang, Tinyi Chu, Lauren A Choate, Charles G Danko

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, R, Tesla K20

June 21, 2017 by hgpu

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim

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Tags: Computer science, CUDA, Deep learning, Keras, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Package, Python, Signal processing, Tesla K80, Tesla M60

June 21, 2017 by hgpu

Efficient OpenCL-based concurrent tasks offloading on accelerators

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Task scheduling, Tesla K20

June 17, 2017 by hgpu

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

C. Escalante, T. Morales de Luna, M.J. Castro

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Mathematics, Numerical Analysis, nVidia

June 17, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

Parallel Monte Carlo on Intel MIC Architecture

Emanouil Atanassov, Todor Gurov, Sofiya Ivanovska, Aneta Karaivanova

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Tags: Intel Xeon Phi, Monte Carlo simulation, Physics

June 17, 2017 by hgpu

Parallel Computing of Particle Trajectory Sonification to Enable Real-Time Interactivity

Jiajun Yang, Thomas Hermann

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Tags: Cluster analysis, nVidia, nVidia GeForce GTX 970, OpenCL, Physics, Vector quantization

June 17, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

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

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

High-Performance Out-of-core Block Randomized Singular Value Decomposition on GPU

Multi-level Parallelism with MPI and OpenACC for CFD Applications

On the Use of a GPU-Accelerated Mobile Device Processor for Sound Source Localization

Panda: A Compiler Framework for Concurrent CPU-GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Efficient OpenCL-based concurrent tasks offloading on accelerators

Non-Hydrostatic Pressure Shallow Flows: GPU Implementation Using Finite-Volume and Finite-Difference Scheme

Device Placement Optimization with Reinforcement Learning

Parallel Monte Carlo on Intel MIC Architecture

Parallel Computing of Particle Trajectory Sonification to Enable Real-Time Interactivity

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Recent source codes

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

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

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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