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DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

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Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Ameer M.S. Abdelhadi, Christos-Savvas Bouganis, George A. Constantinides

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Tags: Computer science, FPGA, Information Retrieval, Machine learning, Nearest neighbour, Package

October 13, 2019 by hgpu

Neural Network Inference on Mobile SoCs

Siqi Wang, Anuj Pathania, Tulika Mitra

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Tags: ARM, Computer science, Heterogeneous systems, Machine learning, Neural networks, SoC

September 8, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

Automated Architecture Design for Deep Neural Networks

Steven Abreu

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Tags: Computer science, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Thesis

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

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Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Muhammad K.A. Hamdan, Diane Rover, John Just

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Tags: Computer science, Deep learning, Machine learning, Neural networks, Package

August 18, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

July 28, 2019 by hgpu

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

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Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

Daniel Nichols, Nathalie-Sofia Tomov, Frank Betancourt, Stanimire Tomov, Kwai Wong, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, HPC, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1050Ti, Package

July 26, 2019 by hgpu

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan

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Tags: AI, Algorithms, Computer science, CUDA, Factorization, Machine learning, nVidia, Package, Tesla M40

July 21, 2019 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

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Neural Network Inference on Mobile SoCs

Demystifying the MLPerf Benchmark Suite

Automated Architecture Design for Deep Neural Networks

Survey and Benchmarking of Machine Learning Accelerators

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

GPU-Accelerated Atari Emulation for Reinforcement Learning

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

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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