high performance computing on graphics processing units: hgpu.org

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Scalable Breadth-First Search on a GPU Cluster

Yuechao Pan, Roger Pearce, John D. Owens

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Tags: Computer science, CUDA, GPU cluster, nVidia, OpenMP, Performance, Tesla P100

March 25, 2018 by hgpu

Optimization of Hierarchical Matrix Computation on GPU

Satoshi Ohshima, Ichitaro Yamazaki, Akihiro Ida, Rio Yokota

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenMP, Tesla P100

March 25, 2018 by hgpu

MALBEC: a new CUDA-C ray-tracer in General Relativity

G. D. Quiroga

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Tags: CUDA, General Relativity and Quantum Cosmology, nVidia, nVidia GeForce GTX 960 M, Package, Physics

March 25, 2018 by hgpu

The VOLNA-OP2 Tsunami Code (Version 1.0)

Istvan Z Reguly, Devaraj Gopinathan, Joakim H Beck, Michael B Giles, Serge Guillas, Frederic Dias

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Tags: CUDA, Earth and Space Sciences, Intel Xeon Phi, nVidia, Package, Tesla P100

March 22, 2018 by hgpu

TBD: Benchmarking and Analyzing Deep Neural Network Training

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan XP, nVidia Quadro P 4000

March 22, 2018 by hgpu

MACC: An OpenACC Transpiler for Automatic Multi-GPU Use

Kazuaki Matsumura, Mitsuhisa Sato, Taisuke Boku, Artur Podobas, Satoshi Matsuoka

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Tags: Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla P100

March 22, 2018 by hgpu

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

Xiaolong Xie, Yun Liang, Xiuhong Li, Wei Tan

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Tags: Algorithms, Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia GeForce GTX Titan XP, Package, Tesla V100

March 17, 2018 by hgpu

NVIDIA Tensor Core Programmability, Performance & Precision

Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Mixed precision, nVidia, Performance, Tesla V100

March 17, 2018 by hgpu

Portable Real-Time DCT Based Steganography Using OpenCL

Ante Poljicak, Guillermo Botella, Carlos Garcia, Luka Kedmenec, Manuel Prieto-Matias

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Tags: APU, ARM, ATI, ATI Radeon HD 6870, Image processing, nVidia, nVidia GeForce GTX 980, OpenCL, real time systems

March 10, 2018 by hgpu

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Lu Li

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla C1060, Tesla C2050, Tesla K20, Tesla M2050, Thesis

March 10, 2018 by hgpu

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Moritz Kreutzer, Georg Hager, Dominik Ernst, Holger Fehske, Alan R. Bishop, Gerhard Wellein

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Tags: Chemistry, Computational Physics, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Package, Performance, Physics, Quantum Physics, Tesla P100

March 10, 2018 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Scalable Breadth-First Search on a GPU Cluster

Optimization of Hierarchical Matrix Computation on GPU

MALBEC: a new CUDA-C ray-tracer in General Relativity

The VOLNA-OP2 Tsunami Code (Version 1.0)

TBD: Benchmarking and Analyzing Deep Neural Network Training

MACC: An OpenACC Transpiler for Automatic Multi-GPU Use

HPVM: Heterogeneous Parallel Virtual Machine

CuLDA_CGS: Solving Large-scale LDA Problems on GPUs

NVIDIA Tensor Core Programmability, Performance & Precision

Portable Real-Time DCT Based Steganography Using OpenCL

Programming Abstractions and Optimization Techniques for GPU-based Heterogeneous Systems

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

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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