high performance computing on graphics processing units: hgpu.org

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Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

Konstantinos Krommydas, Ahmed E. Helal, Anshuman Verma, Wu-Chun Feng

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Tags: Benchmarking, Computer science, FPGA, Intel Xeon Phi, OpenCL, performance portability, Tesla C2070, Tesla K20

May 30, 2016 by hgpu

A GPU Accelerated Continuous and Discontinuous Galerkin Non-hydrostatic Atmospheric Model

Daniel S. Abdi, Lucas C. Wilcox, Timothy Warburton, Francis X. Giraldo

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Tags: Algorithms, CUDA, Earth and Space Sciences, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan Black, OCCA, OpenCL, Tesla K20

May 30, 2016 by hgpu

Deep API Learning

Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim

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Tags: Computer science, CUDA, Deep learning, Information Retrieval, NLP, nVidia, RNN, Tesla K20

May 30, 2016 by hgpu

Data Remanence and Digital Forensic Investigation for CUDA Graphics Processing Units

X. J. A. Bellekens, G. Paul, J. M. Irvine, C. Tachtatzis, R. C. Atkinson, C. Renfrew, T. Kirkham

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Tags: Computer science, data leak, Data recovery, forensic, GPU, Memory, remanence, vulnerable

May 28, 2016 by xhgpu

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

Markus Kammerstetter, Markus Muellner, Daniel Burian, Christian Kudera, Wolfgang Kastner

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 770, nVidia GRID K520, Security

May 28, 2016 by hgpu

An OpenMP Programming Environment on Mobile Devices

Tyng-Yeu Liang, Hung-Fu Li, Yu-Chih Chen

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Tags: Computer science, Heterogeneous systems, LLVM, OpenCL, OpenMP

May 28, 2016 by hgpu

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Vi Ngoc-Nha Tran, Phuong Hoai Ha

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Tags: Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, Sparse matrix

May 28, 2016 by hgpu

Multi-threaded Geant4 on the Xeon-Phi with Complex High-Energy Physics Geometry

Steven Farrell, Andrea Dotti, Makoto Asai, Paolo Calafiura, Romain Monnard

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Tags: Benchmarking, Computational Physics, High Energy Physics - Experiment, Intel Xeon Phi, Physics

May 28, 2016 by hgpu

Theano-MPI: a Theano-based Distributed Training Framework

He Ma, Fei Mao, Graham W. Taylor

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Tags: Computer science, CUDA, Deep learning, Machine learning, MPI, nVidia, Package, Tesla K20, Tesla K80

May 28, 2016 by hgpu

Faster GPU-based convolutional gridding via thread coarsening

Bruce Merry

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Tags: Algorithms, AMD Radeon R9 290X, Astrophysics, ATI, Computer science, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 980, OpenCL

May 26, 2016 by hgpu

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Oren Tadmor, Yonatan Wexler, Tal Rosenwein, Shai Shalev-Shwartz, Amnon Shashua

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Tags: Algorithms, ARM, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance

May 26, 2016 by hgpu

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

Matthew Dixon, Diego Klabjan, Jin Hoon Bang

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

May 26, 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

Applications

Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

A GPU Accelerated Continuous and Discontinuous Galerkin Non-hydrostatic Atmospheric Model

Deep API Learning

Data Remanence and Digital Forensic Investigation for CUDA Graphics Processing Units

Efficient High-Speed WPA2 Brute Force Attacks using Scalable Low-Cost FPGA Clustering

An OpenMP Programming Environment on Mobile Devices

EPEM: A General and Validated Energy Complexity Model for Multithreaded Algorithms

Multi-threaded Geant4 on the Xeon-Phi with Complex High-Energy Physics Geometry

Theano-MPI: a Theano-based Distributed Training Framework

Faster GPU-based convolutional gridding via thread coarsening

Learning a Metric Embedding for Face Recognition using the Multibatch Method

Implementing Deep Neural Networks for Financial Market Prediction on the Intel Xeon Phi

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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