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Compositional Compilation for Sparse, Irregular Data Parallelism

Adam Harries, Michel Steuwer, Murray Cole, Alan Gray, Christophe Dubach

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Tags: Computer science, Data parallelism, nVidia, OpenCL, Sparse matrix, Tesla K40

January 26, 2016 by hgpu

Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search

Gang Mei, Nengxiong Xu, Liangliang Xu

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Tags: Algorithms, Computer science, CUDA, Nearest neighbour, nVidia, nVidia GeForce GT 730 M

January 26, 2016 by hgpu

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Kyungjoo Kim, Sivasankaran Rajamanickam, George Stelle, H. Carter Edwards, Stephen L. Olivier

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Tags: Algorithms, Computer science, Factorization, Intel Xeon Phi, Mathematical Software, Package, Sparse matrix

January 26, 2016 by hgpu

Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer

Alina Sirbu, Ozalp Babaoglu

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Package, Python, Task scheduling

January 26, 2016 by hgpu

Parallel Explicit FEM Algorithms Using GPU’s

Seyed Parsa Banihashemi

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Tags: Algorithms, AMD Radeon R9 280X, ATI, ATI Radeon HD 7970, Computer science, FEM, Finite element method, nVidia, nVidia GeForce GTX Titan, OpenCL, Particle swarm optimization, Performance, Tesla M2090, Thesis

January 22, 2016 by hgpu

Heterogeneous (CPU+GPU) Working-set Hash Tables

Ziaul Choudhury, Suresh Purini

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Tags: Computer science, CUDA, Hashing, Heterogeneous systems, nVidia, Tesla K20

January 22, 2016 by hgpu

Exploring LLVM Infrastructure for Simplified Multi-GPU Programming

Alexander Matz, Mark Hummel, Holger Froning

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Tags: Computer science, CUDA, LLVM, nVidia, OpenCL, Tesla K80

January 22, 2016 by hgpu

Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts

Joshua Dennis Booth, Sivasankaran Rajamanickam, Heidi K. Thornquist

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Tags: Algorithms, Computer science, Factorization, Intel Xeon Phi, Sparse direct solvers, Sparse matrix

January 22, 2016 by hgpu

GPU Multisplit

Saman Ashkiani, Andrew Davidson, Ulrich Meyer, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 750 Ti, Tesla K40

January 22, 2016 by hgpu

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

Mohammed Abdelrahim

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Tags: Computer science, CUDA, Heterogeneous systems, LSTM, Machine learning, nVidia, nVidia GeForce GTX 770, OpenMP, Thesis

January 19, 2016 by hgpu

A framework for efficient execution on GPU and CPU+GPU systems

Jean-Francois Dollinger

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Performance, Thesis

January 19, 2016 by hgpu

Homomorphic Autocomplete

Gizem S. Cetin, Wei Dai, Yarkin Doroz, Berk Sunar

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 690, Security

January 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

Applications

Compositional Compilation for Sparse, Irregular Data Parallelism

Improving GPU-accelerated Adaptive IDW Interpolation Algorithm Using Fast kNN Search

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer

Parallel Explicit FEM Algorithms Using GPU’s

Heterogeneous (CPU+GPU) Working-set Hash Tables

Exploring LLVM Infrastructure for Simplified Multi-GPU Programming

Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts

GPU Multisplit

Adaptive and Hybrid Machine Learning Approaches Utilizing General Purpose Computing on Graphical Processing Units

A framework for efficient execution on GPU and CPU+GPU systems

Homomorphic Autocomplete

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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