high performance computing on graphics processing units: hgpu.org

Tommaso Biagini, Giovanni Chillemi, Gianluigi Mazzoccoli, Alessandro Grottesi, Caterina Fusilli, Daniele Capocefalo, Stefano Castellana, Angelo Luigi Vescovi, Tommaso Mazza

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Tags: Benchmarking, Bioinformatics, Biology, Chemistry, CUDA, Genomics, Molecular dynamics, nVidia, OpenCL, Physics, Tesla C2070

December 19, 2017 by hgpu

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

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Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

POMPEI: Programming with OpenMP4 for Exascale Investigations

Jack Dongarra, Azzam Haidar, Oscar Hernandez, Stanimire Tomov, Manjunath Gorentla Venkata

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, nVidia, OpenACC, OpenMP, Programming techniques, Tesla M2090

December 15, 2017 by hgpu

Effective Extensible Programming: Unleashing Julia on GPUs

Tim Besard, Christophe Foket, Bjorn De Sutter

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Tags: Code generation, Computer science, CUDA, High-level Languages, LLVM, nVidia, nVidia GeForce GTX 1080, Package, Programming Languages

December 15, 2017 by hgpu

Intra-node Memory Safe GPU Co-Scheduling

Carlos Reano, Federico Silla, Dimitrios S. Nikolopoulos, Blesson Varghese

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Tags: Computer science, CUDA, nVidia, Package, Task scheduling, Tesla K20

December 15, 2017 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

Design and Implementation of the Futhark Programming Language

A Generic Inverted Index Framework for Similarity Search on the GPU

Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

An MPI-Based Python Framework for Distributed Training with Keras

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Molecular dynamics recipes for genome research

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

POMPEI: Programming with OpenMP4 for Exascale Investigations

Effective Extensible Programming: Unleashing Julia on GPUs

Intra-node Memory Safe GPU Co-Scheduling

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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