high performance computing on graphics processing units: hgpu.org

Olivier Adjoua, Louis Lagardère, Luc-Henri Jolly, Arnaud Durocher, Thibaut Very, Isabelle Dupays, Zhi Wang, Théo Jaffrelot Inizan, Frédéric Célerse, Pengyu Ren, Jay W. Ponder, Jean-Philip Piquemal

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Tags: Biophysics, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia DGX-A100, OpenACC, Package, Physics, Tesla V100

November 8, 2020 by hgpu

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Raúl Nozal, Jose Luis Bosque, Ramon Beivide

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Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX 950, OpenCL, Package, performance portability, Task scheduling

November 1, 2020 by hgpu

Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Akio Hayakawa, Takuya Narihira

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 1, 2020 by hgpu

Memory Optimization for Deep Networks

Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl

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Tags: Computer science, CUDA, Deep learning, Memory, nVidia, Optimization, Package, PyTorch, Tesla P100

November 1, 2020 by hgpu

FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

Chirag Modi, Francois Lanusse, Uros Seljak

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Tags: Astrophysics, Cosmology, CUDA, N-body simulation, nVidia, Package, Physics, TensorFlow, Tesla V100

October 25, 2020 by hgpu

A Tensor Compiler for Unified Machine Learning Prediction Serving

Supun Nakandala Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla K80, Tesla P100

October 18, 2020 by hgpu

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

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

pplx-garden: Perplexity open source garden for inference technology

RDMA Point-to-Point Communication for LLM Systems

LC Framework

Characterizing the Performance of Parallel Data-Compression Algorithms across Compilers and GPUs

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high performance computing on graphics processing units: hgpu.org

Ginkgo – A Math Library designed for Platform Portability

GPURepair: Automated Repair of GPU Kernels

Adaptive Data Migration in Load-Imbalanced HPC Applications

Runtime Performances Benchmark for Knowledge Graph Embedding Methods

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

AMGCL – A C++ library for efficient solution of large sparse linear systems

Tinker-HP: Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Memory Optimization for Deep Networks

FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

A Tensor Compiler for Unified Machine Learning Prediction Serving

Recent source codes

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

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

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

pplx-garden: Perplexity open source garden for inference technology

LC Framework

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