high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Designing OP2 for GPU architectures

M.B. Giles, G.R. Mudalige, B. Spencer, C. Bertolli, I. Reguly

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Tags: Computer science, nVidia, nVidia GeForce GTX 560 Ti, Optimization, Package, Performance, Tesla C2070

August 11, 2012 by hgpu

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

G.R. Mudalige, M.B. Giles, I. Reguly, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Performance, Tesla C2050, Tesla C2070

August 11, 2012 by hgpu

Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems

Hartwig Anzt, Stanimire Tomov, Jack Dongarra, Vincent Heuveline

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Tags: Computer science, CUDA, nVidia, Performance, Tesla C2050

August 10, 2012 by hgpu

Policy-based Tuning for Performance Portability and Library Co-optimization

Duane Merrill, Michael Garland, Andrew Grimshaw

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Performance

August 8, 2012 by hgpu

Comparison of OpenMP & OpenCL Parallel Processing Technologies

Krishnahari Thouti, S.R.Sathe

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Tags: Benchmarking, Computer science, nVidia, nVidia Quadro FX 3800, OpenCL, OpenMP, Performance

August 6, 2012 by hgpu

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Mickeal Verschoor, Andrei C. Jalba

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Tags: Computer science, Conjugate gradient solver, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Performance, Sparse matrix

August 4, 2012 by hgpu

Performance Analysis of GPU Accelerators with Realizable Utilization of Computational Density

Justin Richardson, Alan D. George, Herman Lam

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Tags: Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GT 240, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C870

August 3, 2012 by hgpu

Interference-driven resource management for GPU-based heterogeneous clusters

Rajat Phull, Cheng-Hong Li, Kunal Rao, Srihari Cadambi, Srimat Chakradhar

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, PC cluster, Performance, Tesla C2050

August 1, 2012 by hgpu

A Comparative Study of OpenACC Implementations

Ruyman Reyes, Ivan Lopez, Juan J. Fumero, Francisco de Sande

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenACC, OpenCL, OpenMP, Performance, Tesla C2050

July 23, 2012 by hgpu

Optimizing MapReduce for GPUs with effective shared memory usage

Linchuan Chen, Gagan Agrawal

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Tags: Computer science, Heterogeneous systems, MapReduce, Memory, nVidia, nVidia Quadro FX 5800, Performance

July 16, 2012 by hgpu

A Yoke of Oxen and a Thousand Chickens for Heavy Lifting Graph Processing

Abdullah Gharaibeh, Lauro Beltrao Costa, Elizeu Santos-Neto, Matei Ripeanu

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Tags: Computer science, CUDA, Graph theory, Heterogeneous systems, nVidia, Optimization, Performance, Programming Languages, Programming techniques, Tesla C2050

July 16, 2012 by hgpu

ab-Stream: A Framework for programming Many-core

Xinbiao Gan, Zhiying Wang, Li Shen, Qi Zhu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

July 15, 2012 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

Designing OP2 for GPU architectures

OP2: An Active Library Framework for Solving Unstructured Mesh-based Applications on Multi-Core and Many-Core Architectures

Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems

Policy-based Tuning for Performance Portability and Library Co-optimization

Comparison of OpenMP & OpenCL Parallel Processing Technologies

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Performance Analysis of GPU Accelerators with Realizable Utilization of Computational Density

Interference-driven resource management for GPU-based heterogeneous clusters

A Comparative Study of OpenACC Implementations

Optimizing MapReduce for GPUs with effective shared memory usage

A Yoke of Oxen and a Thousand Chickens for Heavy Lifting Graph Processing

ab-Stream: A Framework for programming Many-core

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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