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Join Execution Using Fragmented Columnar Indices on GPU and MIC

Elena V. Ivanova, Stepan O. Prikazchikov, Leonid B. Sokolinsky

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Tags: Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, Tesla K40

December 10, 2015 by hgpu

A Parallel Solver for Markov Decision Process in Crowd Simulations

Sergio Ruiz, Benjamin Hernandez

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Tags: ARM, Computer science, Crowd simulation, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Path problems, Tesla K40, Thrust

December 9, 2015 by hgpu

A Semi-Automated Tool Flow for Roofline Anaylsis of OpenCL Kernels on Accelerators

Servesh Muralidharan, Kenneth O'Brien, Christian Lalanne

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Tags: Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Performance, Tesla K20

December 8, 2015 by hgpu

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, Zheng Zhang

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 980, Package

December 8, 2015 by hgpu

Towards Memory-Efficient Answering of Tree-Shaped SPARQL Queries using GPUs

Jedrzej Potoniec, Michal Madziar

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Tags: Computer science, Data mining, Databases, OpenCL, Package

December 8, 2015 by hgpu

Scaling Deep Learning on Multiple In-Memory Processors

Lifan Xu, Dong Ping Zhang, Nuwan Jayasena

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Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, OpenCL

December 8, 2015 by hgpu

Nonlinear Dynamic Analysis Efficiency by Using a GPU Parallelization

Hong-yu Li, Jun Teng, Zuo-hua Li, Lu Zhang

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GT 430, nVidia GeForce GT 720 M, nVidia GeForce GTX 460

December 8, 2015 by hgpu

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

Konstantin Y. Besedin, Pavel S. Kostenetskiy, Stepan O. Prikazchikov

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Tags: Algorithms, Compression, Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, OpenMP, Tesla K40

December 6, 2015 by hgpu

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

Hakan Gokahmetoglu

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Tags: Algorithms, Computational Complexity, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M, OpenMP, Sorting, Thesis

December 6, 2015 by hgpu

CuMF: scale matrix factorization using just ONE machine with GPUs

Wei Tan, Liangliang Cao

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Tags: Computer science, CUDA, Data parallelism, Factorization, Linear Algebra, nVidia, nVidia GeForce GTX Titan X

December 6, 2015 by hgpu

Parallelization Methods of the Template Matching Method on Graphics Accelerators

Gabor Kertesz, Sandor Szenasi, Zoltan Vamossy

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, Tesla K40

December 6, 2015 by hgpu

The Genetic Convolutional Neural Network Model Based on Random Sample

You Zhining, Pu Yunming

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Tags: Algorithms, CNN, Computer science, CUDA, Deep learning, Genetic algorithm, Neural networks, nVidia, nVidia GeForce GTX 770

December 4, 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

Join Execution Using Fragmented Columnar Indices on GPU and MIC

A Parallel Solver for Markov Decision Process in Crowd Simulations

A Semi-Automated Tool Flow for Roofline Anaylsis of OpenCL Kernels on Accelerators

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Towards Memory-Efficient Answering of Tree-Shaped SPARQL Queries using GPUs

Scaling Deep Learning on Multiple In-Memory Processors

Nonlinear Dynamic Analysis Efficiency by Using a GPU Parallelization

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

A Study of Parallel Sorting Algorithms Using CUDA and OpenMP

CuMF: scale matrix factorization using just ONE machine with GPUs

Parallelization Methods of the Template Matching Method on Graphics Accelerators

The Genetic Convolutional Neural Network Model Based on Random Sample

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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