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Parallel Subgraph Mining on Hybrid Platforms: HPC Systems, Multi-Cores and GPUs

Nilothpal Talukder

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Tags: Algorithms, Computer science, CUDA, Data mining, Databases, MPI, nVidia, Tesla C2075, Thesis

April 29, 2016 by hgpu

GPU-Aware Non-contiguous Data Movement In Open MPI

Wei Wu, George Bosilca, Rolf vandeVaart, Sylvain Jeaugey, Jack Dongarra

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Tags: Computer science, CUDA, MPI, nVidia, Tesla K40

April 26, 2016 by hgpu

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

Benjamin Worpitz

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Tags: Computer science, CUDA, Intel Xeon Phi, OpenMP, Package, Particle-in-cell methods, Performance, performance portability, Tesla K20, Tesla K80, Thesis

April 26, 2016 by hgpu

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

Feng Zhang, Jidong Zhai, Wenguang Chen, Bingsheng He, Shuhao Zhang

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Tags: Benchmarking, Computer science, OpenCL, Package

April 26, 2016 by hgpu

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

Ilya Loshchilov, Frank Hutter

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Python

April 26, 2016 by hgpu

GPL: A GPU-based Pipelined Query Processing Engine

Johns Paul, Jiong He, Bingsheng He

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Tags: Analytical model, Computer science, Databases, nVidia, OpenCL

April 24, 2016 by hgpu

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

M. Abalenkovs, A. Abdelfattah, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, A. YarKhan

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Programming techniques, Review, Tesla K40

April 19, 2016 by hgpu

A Parallel Solution to Finding Nodal Neighbors in Generic Meshes

Gang Mei, Nengxiong Xu, Hong Tian

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Tags: Computational Geometry, Computer science, CUDA, nVidia, nVidia GeForce GT 640

April 19, 2016 by hgpu

MIML Learning with CNNs: Yelp Restaurant Photo Classification

Pulkit Agrawal, Raghav Gupta

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Tags: CNN, Computer science, CUDA, Deep learning, Intel Xeon Phi, nVidia

April 19, 2016 by hgpu

A Unified, Hardware-Fitted, Cross-GPU Performance Model

James Stevens, Andreas Klockner

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Tags: AMD Radeon R9 Fury, ATI, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 19, 2016 by hgpu

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Yongchao Liu, Srinivas Aluru

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, PTX, Tesla K40

April 19, 2016 by hgpu

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

Henggang Cui, Hao Zhang, Gregory R. Ganger, Phillip B. Gibbons, Eric P. Xing

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Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Tesla K20

April 16, 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

Parallel Subgraph Mining on Hybrid Platforms: HPC Systems, Multi-Cores and GPUs

GPU-Aware Non-contiguous Data Movement In Open MPI

Investigating performance portability of a highly scalable particle-in-cell simulation code on various multi-core architectures

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

CMA-ES for Hyperparameter Optimization of Deep Neural Networks

GPL: A GPU-based Pipelined Query Processing Engine

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

A Parallel Solution to Finding Nodal Neighbors in Generic Meshes

MIML Learning with CNNs: Yelp Restaurant Photo Classification

A Unified, Hardware-Fitted, Cross-GPU Performance Model

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server

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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