high performance computing on graphics processing units: hgpu.org

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Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, Fredo Durand

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan, Package, Raytracing, Rendering

September 3, 2016 by hgpu

DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python

September 3, 2016 by hgpu

Deep Learning on FPGAs

Griffin James Lacey

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Tags: Computer science, Deep learning, FPGA, nVidia, nVidia Quadro K600, OpenCL, Thesis

August 31, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

Flux tubes at Finite Temperature

Pedro Bicudo, Nuno Cardoso, Marco Cardoso

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Tags: CUDA, High Energy Physics - Lattice, nVidia, Phase transition, Physics, QCD

August 31, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Suyang Zhu, Sunita Chandrasekaran, Peng Sun, Barbara Chapman, Marcus Winter, Tobias Schuele

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Jetson TK1

August 28, 2016 by hgpu

Benchmarking State-of-the-Art Deep Learning Software Tools

Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu

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Tags: Benchmarking, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, RNN, Tesla K80

August 28, 2016 by hgpu

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Dennis Bazow, Ulrich W. Heinz, Michael Strickland

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Tags: Algorithms, Computational Physics, CUDA, Fluid dynamics, High Energy Physics - Phenomenology, Intel Xeon Phi, Nuclear Theory, nVidia, nVidia GeForce GTX 560 M, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan Z, Physics

August 28, 2016 by hgpu

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Christopher P. Stone, Kyle E. Niemeyer

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Tags: Computational Physics, Computer science, Differential equations, Intel Xeon Phi, nVidia, ODEs, OpenCL, Ordinary differential equations, Physics, Tesla K20

August 28, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Ahmed E. Helal, Paul Sathre, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, Intel Xeon Phi, MPI, nVidia, OpenCL, performance portability, Tesla K20

August 23, 2016 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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high performance computing on graphics processing units: hgpu.org

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

DeepPy: Pythonic deep learning

Deep Learning on FPGAs

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Flux tubes at Finite Temperature

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Exploring Task Parallelism for Heterogeneous Systems Using Multicore Task Management API

Benchmarking State-of-the-Art Deep Learning Software Tools

Massively parallel simulations of relativistic fluid dynamics on graphics processing units with CUDA

Accelerating finite-rate chemical kinetics with coprocessors: comparing vectorization methods on GPUs, MICs, and CPUs

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-core Clusters

Recent source codes

CrossTL: Universal Programming Language & Translator

TBD-GPU

DG-SWEM - The Discontinuous Galerkin Shallow Water Equation Model

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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