high performance computing on graphics processing units: hgpu.org

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Splotch: porting and optimizing for the Xeon Phi

Timothy Dykes, Claudio Gheller, Marzia Rivi, Mel Krokos

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Tags: Computer science, CUDA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Package, Tesla K20, Visualization

June 16, 2016 by hgpu

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Re

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

June 16, 2016 by hgpu

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Javier Prades, Blesson Varghese, Carlos Reano, Federico Silla

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Tags: Computer science, CUDA, Finance, nVidia, Tesla K20

June 16, 2016 by hgpu

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Erik Zenker, Rene Widera, Axel Hubl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Package, Particle-in-cell methods, Physics, Plasma physics, Programming techniques, Tesla K80

June 14, 2016 by hgpu

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Guoyang Chen, Huiyang Zhou, Xipeng Shen, Josh Gahm, Narayan Venkat, Skip Booth, John Marshall

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Tags: Algorithms, APU, ATI, Computer science, Error recovery, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

June 14, 2016 by hgpu

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Jan Masek, Radim Burget, Lukas Povoda, Malay Kishore Dutta

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Tags: Algorithms, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 690, OpenCL

June 14, 2016 by hgpu

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Alberto Magni

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7970, Computer science, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Package, performance portability, Tesla K20, Thesis

June 9, 2016 by hgpu

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Yusuke Nagasaka, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

June 9, 2016 by hgpu

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Naila Farooqui

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Tags: ATI, ATI Radeon HD 7770, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, PTX, Tesla C2050, Tesla M2075, Thesis

June 9, 2016 by hgpu

Massively-Parallel Lossless Data Decompression

Evangelia Sitaridi, Rene Mueller, Tim Kaldewey, Guy Lohman, Kenneth Ross

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, Tesla K40

June 7, 2016 by hgpu

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Jeffrey Kelling, Geza Odor, Sibylle Gemming

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Tags: Computational Physics, Computer science, CUDA, Materials Science, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce Titan Black, Statistical Mechanics, Tesla K80

June 7, 2016 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

June 7, 2016 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

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high performance computing on graphics processing units: hgpu.org

Splotch: porting and optimizing for the Xeon Phi

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

OpenCL-Based Erasure Coding on Heterogeneous Architectures

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Analysis and Parameter Prediction of Compiler Transformation for Graphics Processors

Adaptive Multi-level Blocking Optimization for Sparse Matrix Vector Multiplication on GPU

Runtime Specialization for Heterogeneous CPU-GPU Platforms

Massively-Parallel Lossless Data Decompression

Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

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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