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CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Martin Kerhart, Jan Drchal

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Tags: Caffe, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

February 16, 2016 by hgpu

Writing a performance-portable matrix multiplication

Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela

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Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20

February 11, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

FARGO3D: A new GPU-oriented MHD code

Pablo Benitez-Llambay, Frederic Masset

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Tags: Astrophysics, CUDA, Finite difference, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, N-body simulation, nVidia, Package, Physics, Tesla K20

February 10, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

Portable Programming Models for Heterogeneous Platforms

Deepak Majeti

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7660, Computer science, Heterogeneous systems, nVidia, OpenCL, Package, Tesla M2050, Thesis

February 8, 2016 by hgpu

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Gang Mei, Hong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Package

February 6, 2016 by hgpu

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

Yifei Li

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Tags: Algorithms, Clustering, Computer science, Computer vision, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, OpenMP, Package, Thesis, Visualization

February 2, 2016 by hgpu

R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses

Benoit Liquet, Leonardo Bottolo, Gianluca Campanella, Sylvia Richardson, Marc Chadeau-Hyam

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Tags: Bayesian, Computer science, CUDA, CULA, nVidia, Package

February 1, 2016 by hgpu

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

Zdenek Buk

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, Package, RNN

January 29, 2016 by hgpu

GeNN: a code generation framework for accelerated brain simulations

Esin Yavuz, James Turner, Thomas Nowotny

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Tags: Biology, Code generation, CUDA, Neural networks, Neuroscience, Numerical simulation, nVidia, nVidia Quadro K 2000 M, Package, Tesla C2070, Tesla K40

January 29, 2016 by hgpu

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Francisco Javier Ordonez, Daniel Roggen

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Tags: Computer science, Deep learning, LSTM, Machine learning, Neural networks, Package, Python, RNN, Theano

January 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

See all packages

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high performance computing on graphics processing units: hgpu.org

CaffeLink: Mathematica binding for Caffe Deep Learning Framework

Writing a performance-portable matrix multiplication

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

FARGO3D: A new GPU-oriented MHD code

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Portable Programming Models for Heterogeneous Platforms

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Extracting Flow Features Using Bag-of-Features and Supervised Learning Techniques

R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses

GPU-Accelerated Recurrent Neural Networks: OpenCLLink and SymbolicC

GeNN: a code generation framework for accelerated brain simulations

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

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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