high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Study of Bandwidth Partitioning for Co-executing GPU Kernels

Erik Melander

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Thesis

December 7, 2017 by hgpu

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Shaohuai Shi, Xiaowen Chu

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Tags: Algorithms, Benchmarking, Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, Package, Performance, TensorFlow, Tesla P40, Torch

November 26, 2017 by hgpu

Launch-time Optimization of OpenCL Kernels

Andrew Siu Doug Lee

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Tags: Computer science, LLVM, nVidia, nVidia GeForce GTX 780, OpenCL, Performance, Thesis

November 16, 2017 by hgpu

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Peter Boyle, Michael Chuvelev, Guido Cossu, Christopher Kelly, Christoph Lehner, Lawrence Meadows

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, MPI, OpenMP, Package, Performance, Physics

November 16, 2017 by hgpu

Performance Evaluation of Deep Learning Tools in Docker Containers

Pengfei Xu, Shaohuai Shi, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Performance

November 12, 2017 by hgpu

Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs

G. D. Balogh, I. Z. Reguly, G. R. Mudalige

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, OpenACC, OpenMP, Performance, Tesla K40, Tesla P100

November 7, 2017 by hgpu

Acceleration of tensor-product operations for high-order finite element methods

Kasia Swirydowicz, Noel Chalmers, Ali Karakus, Timothy Warburton

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Tags: Computer science, CUDA, FEM, Finite element method, Mathematical Software, Numerical Analysis, nVidia, Overview, Performance, Tesla P100

November 7, 2017 by hgpu

Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores

Yong-Xian Wang, Li-Lun Zhang, Wei Liu, Xing-Hua Cheng, Yu Zhuang, Anthony T. Chronopoulos

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenMP, Performance, supercomputer, Tesla M2050, Xeon Phi

October 31, 2017 by hgpu

A Fast and Generic GPU-Based Parallel Reduction Implementation

Walid Jradi, Hugo do Nascimento, Wellington Martins

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, Tesla C2075

October 24, 2017 by hgpu

Computation of gray-level co-occurrence matrix based on CUDA and its optimization

Huichao Hong, Lixin Zheng, Shuwan Pan

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 1050 Ti, Performance

October 21, 2017 by hgpu

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

Holger Homann, Francois Laenen

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Tags: Benchmarking, Computational Physics, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 755 M, Package, Performance, Physics, Tesla M2050

October 15, 2017 by hgpu

An Efficient Load Balancing Method for Tree Algorithms

Osama Talaat Ibrahim, Ahmed El-Mahdy

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Tags: Algorithms, Computer science, Intel Xeon Phi, load balancing, Performance

October 3, 2017 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

Study of Bandwidth Partitioning for Co-executing GPU Kernels

Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Launch-time Optimization of OpenCL Kernels

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Performance Evaluation of Deep Learning Tools in Docker Containers

Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs

Acceleration of tensor-product operations for high-order finite element methods

Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores

A Fast and Generic GPU-Based Parallel Reduction Implementation

Computation of gray-level co-occurrence matrix based on CUDA and its optimization

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

An Efficient Load Balancing Method for Tree Algorithms

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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