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hgpu.org » Programming » CUDA

GPUvm: Why Not Virtualizing GPUs at the Hypervisor?

Yusuke Suzuki, Shinpei Kato, Hiroshi Yamada, Kenji Kono

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia Quadro 6000, Operating systems, Virtualization

July 4, 2014 by hgpu

Exploiting parallel features of modern computer architectures in bioinformatics: applications to genetics, structure comparison and large graph analysis

Guillaume Chapuis

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Genetics, Graph theory, nVidia, Path problems, Tesla C2050, Thesis

July 3, 2014 by hgpu

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

Pisit Makpaisit, Putchong Uthayopas

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Tags: Compilers, Computer science, CUDA, GPU cluster, nVidia, OpenACC, Tesla M2050

July 3, 2014 by hgpu

Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

Shahzeb Siddiqui, Saber Feki

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Tags: Computer science, CUDA, nVidia, OpenACC, Performance, Tesla K20

July 3, 2014 by hgpu

Reducing the Code Degree Of Parallelism to Increase GPUs Reliability

P. Rech, L. Carro

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Tags: Computer science, CUDA, Fault simulation, nVidia, nVidia GeForce GTX 480, Tesla C2050

July 3, 2014 by hgpu

Toward Auto-tuned Krylov Basis Computations with minimized Communication on Clusters of Accelerators

Langshi Chen, Serge Petiton, Leroy Drummond, Maxime Hugues

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Tesla K20

July 3, 2014 by hgpu

Energy Efficiency Benefits of Reducing the Voltage Guardband on the Kepler GPU Architecture

Jingwen Leng, Yazhou Zu, Vijay Janapa Reddi

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 680

July 1, 2014 by hgpu

Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering

Linus Kallberg, Thomas Larsson

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia Quadro K 4000 M

July 1, 2014 by hgpu

Fast Galactic Structure Finding using Graphics Processing Units

Daniel Wood

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Tags: Astrophysics, CUDA, Gravitation, KD-tree, N-body simulation, nVidia, nVidia GeForce GTX 260 Ti, nVidia GeForce GTX 460 Ti

July 1, 2014 by hgpu

The design and verification of Mumax3

Arne Vansteenkiste, Jonathan Leliaert, Mykola Dvornik, Felipe Garcia-Sanchez, Bartel Van Waeyenberge

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Source codes

Tags: Computational Physics, CUDA, Image generation, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX Titan, Package, Physics

July 1, 2014 by hgpu

Modified Bloom filter for high performance hybrid NoSQL systems

A.B. Vavrenyuk, N.P. Vasilyev, V.V. Makarov, K.A. Matyukhin, M.M. Rovnyagin, A.A. Skitev

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Tags: Bloom filter, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 260

June 28, 2014 by hgpu

Implementation of the genetic algorithm by means of CUDA technology involved in travelling salesman problem

Anna Plichta, Tomasz Gaciarz, Bartosz Baranowski, Szymon Szominski

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 285

June 28, 2014 by hgpu

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

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

Programming

Exploiting parallel features of modern computer architectures in bioinformatics: applications to genetics, structure comparison and large graph analysis

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

Reducing the Code Degree Of Parallelism to Increase GPUs Reliability

Toward Auto-tuned Krylov Basis Computations with minimized Communication on Clusters of Accelerators

Energy Efficiency Benefits of Reducing the Voltage Guardband on the Kepler GPU Architecture

Accelerated Computation of Minimum Enclosing Balls by GPU Parallelization and Distance Filtering

Fast Galactic Structure Finding using Graphics Processing Units

The design and verification of Mumax3

Modified Bloom filter for high performance hybrid NoSQL systems

Implementation of the genetic algorithm by means of CUDA technology involved in travelling salesman problem

Recent source codes

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)

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

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