high performance computing on graphics processing units: hgpu.org

Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi

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

February 28, 2017 by hgpu

CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi

Andre Viebke, Suejb Memeti, Sabri Pllana, Ajith Abraham

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Tags: CNN, Computer science, Deep learning, Intel Xeon Phi, Neural networks, OpenMP

February 28, 2017 by hgpu

Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network

Jialiang Zhang, Jing Li

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Tags: Computer science, Computer vision, Deep learning, FPGA, Neural networks, OpenCL

February 26, 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

Decoupled Block-Wise ILU(k) Preconditioner on GPU

Wireless Interference Identification with Convolutional Neural Networks

Multi-kernel Data Partitioning with Channel on OpenCL-based FPGAs

Performance and Portability of Accelerated Lattice Boltzmann Applications with OpenACC

Improving the Neural GPU Architecture for Algorithm Learning

Billion-scale similarity search with GPUs

An Efficient Multiway Mergesort for GPU Architectures

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Key Reconciliation with Low-Density Parity-Check Codes for Long-Distance Quantum Cryptography

Deep Voice: Real-time Neural Text-to-Speech

CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi

Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network

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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