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Devito: automated fast finite difference computation

Navjot Kukreja, Mathias Louboutin, Felippe Vieira, Fabio Luporini, Michael Lange, Gerard Gorman

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Tags: Computer science, Finite difference, Intel Xeon Phi, OpenMP, Package

September 17, 2016 by hgpu

The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better

Christopher Parker, Matthew Daiter, Kareem Omar, Gil Levi, Tal Hassner

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1080, Package

September 17, 2016 by hgpu

Parallel Dynamics Computation using Prefix Sum Operations

Yajue Yang, Yuanqing Wu, Jia Pan

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

September 17, 2016 by hgpu

A parallel pattern for iterative stencil + reduce

M. Aldinucci, M. Danelutto, M. Drocco, P. Kilpatrick, C. Misale, G. Peretti Pezzi, M. Torquati

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Tags: ARM, Computer science, nVidia, OpenCL, Stencil computation, Tesla K40, Tesla M2090

September 17, 2016 by hgpu

Efficient softmax approximation for GPUs

Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Herve Jegou

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Tags: Computer science, Deep learning, LSTM, Neural networks, NLP, nVidia, Tesla K40

September 17, 2016 by hgpu

Data Analysis of Minimally-Structured Heterogeneous Logs: An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes

Chang Liu

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Tags: Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, Package, RNN, Thesis

September 13, 2016 by hgpu

OpenMP as a High-Level Specification Language for Parallelism And its use in Evaluating Parallel Programming Systems

Max Grossman, Jun Shirako, Vivek Sarkar

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Tags: Code generation, Compilers, Computer science, CUDA, LLVM, nVidia, OpenMP, Package

September 13, 2016 by hgpu

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

Olivier Beaumont, Terry Cojean, Lionel Eyraud-Dubois, Abdou Guermouche, Suraj Kumar

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Tags: Algorithms, Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40

September 13, 2016 by hgpu

GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

Muaaz Awan, Fahad Saeed

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Tags: Algorithms, big data, Computer science, CUDA, nVidia, Package, Sorting, Tesla K40

September 10, 2016 by hgpu

System Design Principles for Heterogeneous Resource Management and Scheduling in Accelerator-Based Systems

Dipanjan Sengupta

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Tags: Algorithms, Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro 2000, nVidia Quadro 4000, Task scheduling, Tesla C2050, Tesla C2070, Tesla K20, Thesis

September 10, 2016 by hgpu

3D Object Recognition with Convolutional Neural Networks

Alberto Garcia-Garcia

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Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GT 730, nVidia GeForce GTX Titan X, Tesla K40, Thesis

September 10, 2016 by hgpu

OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

Christian T. Jacobs, Satya P. Jammy, Neil D. Sandham

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Tags: cfd, Code generation, Computer science, CUDA, Finite difference, Fluid dynamics, Mathematical Software, MPI, nVidia, OpenCL, Tesla K40

September 10, 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

Devito: automated fast finite difference computation

The CUDA LATCH Binary Descriptor: Because Sometimes Faster Means Better

Parallel Dynamics Computation using Prefix Sum Operations

A parallel pattern for iterative stencil + reduce

Efficient softmax approximation for GPUs

Data Analysis of Minimally-Structured Heterogeneous Logs: An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes

OpenMP as a High-Level Specification Language for Parallelism And its use in Evaluating Parallel Programming Systems

Scheduling of Linear Algebra Kernels on Multiple Heterogeneous Resources

GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

3D Object Recognition with Convolutional Neural Networks

OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures

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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