high performance computing on graphics processing units: hgpu.org

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Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications

Naila Farooqui, Christopher J. Rossbach, Yuan Yu, Karsten Schwan

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Tags: Benchmarking, Computer science, CUDA, nVidia, PTX, Tesla M2075

October 11, 2014 by hgpu

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Luigi Nardi, Bruno Bodin, M. Zeeshan Zia, John Mawer, Andy Nisbet, Paul H. J. Kelly, Andrew J. Davison, Mikel Lujan, Michael F. P. O'Boyle, Graham Riley, Nigel Topham, Steve Furber

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Tags: Algorithms, ARM, Benchmarking, Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance

October 10, 2014 by hgpu

Code Refinement of Stencil Codes

Marcel Koster, Roland Leissa, Sebastian Hack

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Tags: Algorithms, Computer science, CUDA, DSL, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680

October 10, 2014 by hgpu

Parallel implementation of linear repetitive processes identification using subspace algorithms

Dominik Kujawa

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460

October 10, 2014 by hgpu

Accelerating Protein Coordinate Conversion using GPUs

Mahsa Bayati, Jaydeep P. Bardhan, David M. King, Miriam Leeser

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Tesla C2075

October 10, 2014 by hgpu

FDTD on Distributed Heterogeneous Multi-GPU Systems

Eirik Myklebost

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Tags: Algorithms, Benchmarking, Cluster computing, CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, nVidia GeForce GTX 760, Physics, Tesla K20, Thesis, Ultrasound

October 10, 2014 by hgpu

cuDNN: Efficient Primitives for Deep Learning

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Tesla K40

October 8, 2014 by hgpu

Movement Tracking in Terrain Conditions Accelerated with CUDA

Piotr Sklodowski, Witold Zorski

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Tags: CUDA, Image processing, nVidia, nVidia GeForce GTX 650 Ti

October 8, 2014 by hgpu

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Mathematical Software, nVidia, Package, Tesla K20

October 8, 2014 by hgpu

A Framework for the Volumetric Integration of Depth Images

Victor Adrian Prisacariu, Olaf Kahler, Ming Ming Cheng, Julien Valentin, Philip H.S. Torr, Ian D. Reid, David W. Murray

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Tags: Computer science, Computer vision, CUDA, nVidia

October 8, 2014 by hgpu

A new ray-tracing scheme for 3D diffuse radiation transfer on highly parallel architectures

Satoshi Tanaka, Kohji Yoshikawa, Takashi Okamoto, Kenji Hasegawa

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, Numerical simulation, nVidia, Raytracing, Tesla M2090

October 8, 2014 by hgpu

Redução de Complexidade de Tempo em GPUs

Ricardo Nobre, Tiago Carneiro, Marcos Negreiros, Felipe Martins Muller

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Tags: GPU, paralelismo, problema da pesquisa, Redução de complexidade, speedup

October 8, 2014 by rhnobre

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

Applications

Leo: A Profile-Driven Dynamic Optimization Framework for GPU Applications

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM

Code Refinement of Stencil Codes

Parallel implementation of linear repetitive processes identification using subspace algorithms

Accelerating Protein Coordinate Conversion using GPUs

FDTD on Distributed Heterogeneous Multi-GPU Systems

cuDNN: Efficient Primitives for Deep Learning

Movement Tracking in Terrain Conditions Accelerated with CUDA

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

A Framework for the Volumetric Integration of Depth Images

A new ray-tracing scheme for 3D diffuse radiation transfer on highly parallel architectures

Redução de Complexidade de Tempo em GPUs

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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