high performance computing on graphics processing units: hgpu.org

Programming

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SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

Eleftherios Avramidis, Marta Lalik, Ozgur E. Akman

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Tags: AMD FirePro W8100, ATI, Computer science, Differential equations, Mathematical Software, nVidia, OpenCL, Package, Tesla V100

August 18, 2019 by hgpu

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Abhinav Muta, Prabhu Ramachandran, Pawan Negi

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Tags: Benchmarking, Computational Physics, Fluid dynamics, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1070 Ti, OpenCL, Package, Poisson equation

August 11, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

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Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Tesla K40

August 11, 2019 by hgpu

Parallelization of Coherent Point Drift for patient registration

Erik Smistad

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, OpenCL, Package, Thesis

August 5, 2019 by hgpu

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Thomas Faict, Erik H. D'Hollander, Bart Goossens

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Tags: Algorithms, Benchmarking, Computer science, FPGA, Heterogeneous systems, OpenCL

August 5, 2019 by hgpu

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

Luiz H. Sena, Iury V. Bessa, Mikhail R. Gadelha, Lucas C. Cordeiro, Edjard Mota

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Tags: Computer science, CUDA, Neural networks, nVidia, Package

August 5, 2019 by hgpu

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Shaoyi Peng, Sheldon X.-D. Tan

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Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

August 5, 2019 by hgpu

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

July 28, 2019 by hgpu

NNS: The Case For Neural Network-based Sorting

Xiao-ke Zhu, Tai-ning Chen, Jing He, Wei Zhou

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Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Sorting, TensorFlow

July 28, 2019 by hgpu

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

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Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 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

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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high performance computing on graphics processing units: hgpu.org

Programming

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Performance Comparison for Neuroscience Application Benchmarks

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Parallelization of Coherent Point Drift for patient registration

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

GPU-Accelerated Atari Emulation for Reinforcement Learning

NNS: The Case For Neural Network-based Sorting

Recent source codes

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

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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