high performance computing on graphics processing units: hgpu.org

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Investigating SRAM PUFs in large CPUs and GPUs

Pol Van Aubel, Daniel J. Bernstein, Ruben Niederhagen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Security

August 1, 2015 by hgpu

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

Prospects of GPGPU in the Auger Offline Software Framework

Tobias Winchen, Marvin Gottowik, Julian Rautenberg

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Tags: Astrophysics, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 750 Ti, Tesla M2090

July 29, 2015 by hgpu

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

Masato Mimura, Shinsuke Sakai, Tatsuya Kawahara

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Speech recognition

July 29, 2015 by hgpu

OKL: A Unified Language for Parallel Architectures

David Medina

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Tags: ATI, ATI Radeon HD 7990, Computer science, CUDA, High-level Languages, nVidia, OpenCL, Tesla K40, Thesis

July 29, 2015 by hgpu

Processing Large-scale XML Files on GPGPU Cluster

Ping Liu

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Tags: Computer science, CUDA, GPU cluster, nVidia, OpenMPI, Performance, Tesla M2075

July 27, 2015 by hgpu

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

Hamed Khakzad, Yasaman Karami, Seyed Shahriar Arab

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Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Particle swarm optimization, Sequence alignment, Tesla C1060

July 24, 2015 by hgpu

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Baoguang Shi, Xiang Bai, Cong Yao

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, Tesla K40

July 24, 2015 by hgpu

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

Keiron O'Shea

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 580

July 24, 2015 by hgpu

On the use of deep Boltzmann machines for road signs classification

Carlo D'Eramo

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Tags: Computer science, Computer vision, CUDA, Image recognition, Machine learning, nVidia, Python, Tesla C1060, Thesis

July 22, 2015 by hgpu

An algebraic parallel treecode in arbitrary dimensions

William B. March, Bo Xiao, Chenhan D. Yu, George Biros

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Linear Algebra, MPI, nVidia, Tesla K40

July 22, 2015 by hgpu

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

Srajan Paliwal, Saurabh Tiwary, Bhaskar Chaudhury, Manish K. Gupta

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Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla K40

July 22, 2015 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

Investigating SRAM PUFs in large CPUs and GPUs

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Prospects of GPGPU in the Auger Offline Software Framework

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

OKL: A Unified Language for Parallel Architectures

Processing Large-scale XML Files on GPGPU Cluster

Accelerating Protein Structure Prediction using Particle Swarm Optimization on GPU

An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Massively Deep Artificial Neural Networks for Handwritten Digit Recognition

On the use of deep Boltzmann machines for road signs classification

An algebraic parallel treecode in arbitrary dimensions

Generating Binary Optimal Codes Using Heterogeneous Parallel Computing

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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