high performance computing on graphics processing units: hgpu.org

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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

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

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

PROJECTION Algorithm for Motif Finding on GPUs

Jhoirene B. Clemente, Francis George C. Cabarle, Henry N. Adorna

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Tags: Algorithms, Biology, Computational biology, CUDA, nVidia

May 26, 2016 by hgpu

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Philipp Gysel

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla K40, Thesis

May 23, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

Graphics Supercomputing Applied to Brain Image Analysis with NiftyReg

Francisco Nurudin Alvarez Gonzalez

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Tags: CUDA, Image processing, Image registration, Medicine, nVidia, nVidia GeForce GTX 980, Thesis

May 23, 2016 by hgpu

A Practical Performance Model for Compute and Memory Bound GPU Kernels

Elias Konstantinidis, Yiannis Cotronis

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Tags: Benchmarking, CUDA, OpenCL, Performance prediction

May 23, 2016 by ekondis

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

GPU-based Pedestrian Detection for Autonomous Driving

Victor Campmany Canes

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Vehicular traffic

May 21, 2016 by hgpu

Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

Neetu Faujdar, SatyaPrakash Ghrera

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

May 21, 2016 by hgpu

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

Ebad Salehi

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Tags: Compression, Computer science, CUDA, nVidia, OpenACC, OpenCL, Package, Tesla K20, Thesis

May 21, 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

Deep API Learning

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

Theano-MPI: a Theano-based Distributed Training Framework

PROJECTION Algorithm for Motif Finding on GPUs

Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Graphics Supercomputing Applied to Brain Image Analysis with NiftyReg

A Practical Performance Model for Compute and Memory Bound GPU Kernels

Architecture-Adaptive Code Variant Tuning

GPU-based Pedestrian Detection for Autonomous Driving

Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

Employing Directive Based Compression Solutions on Accelerators Global Memory under OpenACC

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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