high performance computing on graphics processing units: hgpu.org

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GIFT: A Real-time and Scalable 3D Shape Search Engine

Song Bai, Xiang Bai, Zhichao Zhou, Zhaoxiang Zhang, Longin Jan Latecki

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Tags: Computer science, Computer vision, Deep learning

April 9, 2016 by hgpu

Optimizing Performance of Recurrent Neural Networks on GPUs

Jeremy Appleyard, Tomas Kocisky, Phil Blunsom

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Tags: Computer science, CUDA, Deep learning, Lua, Neural networks, nVidia, Package, RNN, Tesla M40

April 9, 2016 by hgpu

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

Steven Eliuk, Cameron Upright, Anthony Skjellum

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80

April 9, 2016 by hgpu

An Exploratory Study of High Performance Graphics Application Programming Interfaces

Joseph Shiraef

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Tags: AMD Radeon R9 290X, ATI, Computer science, DirectX, nVidia, nVidia GeForce GTX 970, OpenGL, Performance, Thesis, Vulkan

April 6, 2016 by hgpu

gpucc: an open-source GPGPU compiler

Jingyue Wu, Artem Belevich, Eli Bendersky, Mark Heffernan, Chris Leary, Jacques Pienaar, Bjarke Roune, Rob Springer, Xuetian Weng, Robert Hundt

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Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, Presentation, Tesla K40

April 6, 2016 by hgpu

Automatic test case reduction of randomly generated OpenCL kernels

Moritz Pflanzer

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Tags: Code generation, Computer science, LLVM, nVidia, nVidia GeForce GT 630, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, nVidia Quadro K5200, OpenCL, Thesis

April 6, 2016 by hgpu

Are Very Deep Neural Networks Feasible on Mobile Devices?

S. Rallapalli, H. Qiu, A. J. Bency, S. Karthikeyan, R. Govindan, B.S.Manjunath, R. Urgaonkar

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Tags: CNN, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia Jetson TK1

April 6, 2016 by hgpu

Implementation of the SYCL Heterogeneous Computing Library

Peter Zuzek

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Tags: Code generation, Computer science, Heterogeneous systems, OpenCL, Package, SYCL, Thesis

April 3, 2016 by hgpu

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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Tags: Computer science, Deep learning, Finance, Intel Xeon Phi, Neural networks

April 3, 2016 by hgpu

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

Zhaokui Li, Jianbin Fang, Tao Tang, Xuhao Chen, Cheng Chen, Canqun Yang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, Performance

April 3, 2016 by hgpu

A Novel CSR-Based Sparse Matrix-Vector Multiplication on GPUs

Guixia He, Jiaquan Gao

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050

March 29, 2016 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

GIFT: A Real-time and Scalable 3D Shape Search Engine

Optimizing Performance of Recurrent Neural Networks on GPUs

dMath: A Scalable Linear Algebra and Math Library for Heterogeneous GP-GPU Architectures

An Exploratory Study of High Performance Graphics Application Programming Interfaces

gpucc: an open-source GPGPU compiler

Automatic test case reduction of randomly generated OpenCL kernels

Are Very Deep Neural Networks Feasible on Mobile Devices?

Implementation of the SYCL Heterogeneous Computing Library

Classiffication-based Financial Markets Prediction using Deep Neural Networks

Dynamic Sparse-Matrix Allocation on GPUs

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

A Novel CSR-Based Sparse Matrix-Vector Multiplication on 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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