high performance computing on graphics processing units: hgpu.org

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Tensor Contractions with Extended BLAS Kernels on CPU and GPU

Yang Shi, U. N. Niranjan, Animashree Anandkumar, Cris Cecka

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Tags: Algorithms, BLAS, Computer science, CUDA, FFT, Linear Algebra, nVidia, Tesla K40

June 22, 2016 by hgpu

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Maohua Zhu, Liu Liu, Chao Wang, Yuan Xie

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Tags: CNN, Computer science, CUDA, Deep learning, FPGA, MapReduce, Neural networks, nVidia, OpenCL, Tesla K40

June 22, 2016 by hgpu

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

Kevin Andryc, Tedy Thomas, Russell Tessier

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Tags: Benchmarking, Computer science, CUDA, DSP, FPGA

June 22, 2016 by hgpu

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

Igor Korkin, Iwan Nesterow

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Tags: Computer science, CUDA, nVidia, Security

June 21, 2016 by hgpu

A Parallel Algorithm for LZW Decompression, with GPU Implementation

Shunji Funasaka, Koji Nakano, Yasuaki Ito

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Tags: Compression, CUDA, nVidia, nVidia GeForce GTX 980

June 21, 2016 by funasaka

Splotch: porting and optimizing for the Xeon Phi

Timothy Dykes, Claudio Gheller, Marzia Rivi, Mel Krokos

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Tags: Computer science, CUDA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Package, Tesla K20, Visualization

June 16, 2016 by hgpu

NCAM: Near-Data Processing for Nearest Neighbor Search

Vincent T. Lee, Carlo C. del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin, Ali Farhadi

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Tags: Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

June 16, 2016 by hgpu

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Christopher Re

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

June 16, 2016 by hgpu

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Javier Prades, Blesson Varghese, Carlos Reano, Federico Silla

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Tags: Computer science, CUDA, Finance, nVidia, Tesla K20

June 16, 2016 by hgpu

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Erik Zenker, Rene Widera, Axel Hubl, Guido Juckeland, Andreas Knupfer, Wolfgang E. Nagel, Michael Bussmann

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Package, Particle-in-cell methods, Physics, Plasma physics, Programming techniques, Tesla K80

June 14, 2016 by hgpu

Processing Big Data in Main Memory and on GPU

Meisam Fathi Salmi

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Tags: big data, Computer science, CUDA, Hadoop, Java, OpenCL, Scala, Spark, Thesis

June 14, 2016 by hgpu

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

Jan Masek, Radim Burget, Lukas Povoda, Malay Kishore Dutta

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Tags: Algorithms, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 690, OpenCL

June 14, 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

Tensor Contractions with Extended BLAS Kernels on CPU and GPU

CNNLab: a Novel Parallel Framework for Neural Networks using GPU and FPGA-a Practical Study with Trade-off Analysis

Soft GPGPUs for Embedded FPGAs: An Architectural Evaluation

Acceleration of Statistical Detection of Zero-day Malware in the Memory Dump Using CUDA-enabled GPU Hardware

A Parallel Algorithm for LZW Decompression, with GPU Implementation

Splotch: porting and optimizing for the Xeon Phi

NCAM: Near-Data Processing for Nearest Neighbor Search

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

Multi-Tenant Virtual GPUs for Optimising Performance of a Financial Risk Application

Performance-Portable Many-Core Plasma Simulations: Porting PIConGPU to OpenPower and Beyond

Processing Big Data in Main Memory and on GPU

Multi-GPU Implementation of Machine Learning Algorithm using CUDA and OpenCL

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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