high performance computing on graphics processing units: hgpu.org

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Efficient Convolutional Patch Networks for Scene Understanding

Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler

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Tags: CNN, Computer science, Deep learning, Machine learning, OpenCL, Package

July 24, 2015 by hgpu

Parallel Programming in Actor-Based Applications via OpenCL

Paul Harvey, Kristian Hentschel, Joseph Sventek

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Tags: AMD Radeon R9 290X, ATI, Computer science, OpenACC, OpenCL, Package, Performance

July 20, 2015 by hgpu

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Mark James Abraham, Teemu Murtola, Roland Schulz, Szilard Pall, Jeremy C. Smith, Berk Hess, Erik Lindahl

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Tags: Algorithms, Biomolecules, Chemistry, CUDA, Heterogeneous systems, Molecular simulation, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Tesla K40

July 20, 2015 by hgpu

Scaling Monte Carlo Tree Search on Intel Xeon Phi

S. Ali Mirsoleimani, Aske Plaat, Jaap van den Herik, Jos Vermaseren

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Tags: Algorithms, Artificial intelligence, Computer science, Intel Xeon Phi, Package

July 17, 2015 by hgpu

Concurrent Manipulation of Dynamic Data Structures in OpenCL

Henk Mulder

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Tags: Algorithms, Computer science, Data Structures and Algorithms, nVidia, nVidia GeForce GT 310 M, OpenCL, Package

July 15, 2015 by hgpu

Optimization, Specification and Verification of the Prefix Sum Program in an OpenCL Environment

Thijs Wiefferink

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 770, OpenCL, Package

July 13, 2015 by hgpu

Towards Good Practices for Very Deep Two-Stream ConvNets

Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, MPI, nVidia, OpenMPI, Package

July 10, 2015 by hgpu

Autotuning OpenACC Work Distribution via Direct Search

Calvin Montgomery, Jeffrey L. Overbey, Xuechao Li

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Tags: Computer science, MPI, nVidia, OpenACC, Package, Performance, Python, Tesla K20

July 8, 2015 by hgpu

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, OpenCL, Package, Python

July 6, 2015 by hgpu

Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores

Kai Zhang, Kaibo Wang, Yuan Yuan, Lei Guo, Rubao Lee, Xiaodong Zhang

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Tags: Computer science, CUDA, Data parallelism, Databases, nVidia, nVidia GeForce GTX 780, Package

July 6, 2015 by hgpu

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

Carsten Kutzner, Szilard Pall, Martin Fechner, Ansgar Esztermann, Bert L. de Groot, Helmut Grubmuller

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Tags: Benchmarking, Biological Physics, Biomolecules, Computational Physics, Computer science, Molecular dynamics, Molecular simulation, MPI, nVidia, Package, Performance, Tesla K20, Tesla K40

July 6, 2015 by hgpu

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

Amir Gholami, Judith Hill, Dhairya Malhotra, George Biros

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Tags: Algorithms, Computer science, CUDA, FFT, MPI, nVidia, OpemMP, Package, Tesla K20

June 30, 2015 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

Efficient Convolutional Patch Networks for Scene Understanding

Parallel Programming in Actor-Based Applications via OpenCL

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Scaling Monte Carlo Tree Search on Intel Xeon Phi

Concurrent Manipulation of Dynamic Data Structures in OpenCL

Optimization, Specification and Verification of the Prefix Sum Program in an OpenCL Environment

Towards Good Practices for Very Deep Two-Stream ConvNets

Autotuning OpenACC Work Distribution via Direct Search

High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

AccFFT: A library for distributed-memory FFT on CPU and GPU architectures

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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