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Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

Achim Basermann, Melven Rohrig-Zollner, Joachim Illmer

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Tags: CUDA, Fluid dynamics, Fortran, nVidia, Performance, Python, Tesla C2075

December 22, 2015 by hgpu

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Dumitrel Loghin, Lavanya Ramapantulu, Oana Barbu, Yong Meng Teo

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 750 Ti, nVidia Jetson TK1

December 22, 2015 by hgpu

Autotuning Stencils Codes with Algorithmic Skeletons

Chris Cummins

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Package

December 19, 2015 by hgpu

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Carles Montoliu Alvaro

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 560, nVidia GeForce GTX Titan, Thesis

December 19, 2015 by hgpu

Investigation of the SYCL for OpenCL Programming Model

Tim Beattie

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Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, OpenCL, Performance, Thesis

December 19, 2015 by hgpu

Efficient Query Processing in Co-Processor-accelerated Databases

Sebastian Bress

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Tags: Computer science, CUDA, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 640, Package, Tesla C1060, Tesla C2050, Thesis

December 19, 2015 by hgpu

Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

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Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

Origami: A Convolutional Network Accelerator

Lukas Cavigelli, David Gschwend, Christoph Mayer, Samuel Willi, Beat Muheim, Luca Benini

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Tags: Algorithms, Artificial intelligence, ASIC, Computer science, Computer vision, Machine learning, Neural networks

December 15, 2015 by hgpu

Adaptive algebraic multigrid on SIMD architectures

Simon Heybrock, Matthias Rottmann, Peter Georg, Tilo Wettig

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Tags: Algorithms, Computational Physics, High Energy Physics - Lattice, Intel Xeon Phi, Physics

December 15, 2015 by hgpu

A CUDA Kernel Scheduler Exploiting Static Data Dependencies

Eva Burrows

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Tags: Computer science, CUDA, nVidia, Task scheduler

December 15, 2015 by hgpu

Run-time support for multi-level disjoint memory address spaces

Javier Bueno Hedo

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Tags: Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 480, OmpSs, OpenMP, Thesis

December 15, 2015 by hgpu

Bigger Buffer k-d Trees on Multi-Many-Core Systems

Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes

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Tags: Computer science, KD-tree, Nearest neighbour, nVidia, nVidia GeForce GTX Titan Z, OpenCL, Package

December 15, 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

Applications

Performance and Productivity of Parallel Python Programming: A study with a CFD Test Case

A time-energy performance analysis of MapReduce on heterogeneous systems with GPUs

Autotuning Stencils Codes with Algorithmic Skeletons

Study, Modelling and Implementation of the Level Set Method Used in Micromachining Processes

Investigation of the SYCL for OpenCL Programming Model

Efficient Query Processing in Co-Processor-accelerated Databases

Challenges Adapting CUDA PIC Codes to multiple GPUs

Origami: A Convolutional Network Accelerator

Adaptive algebraic multigrid on SIMD architectures

A CUDA Kernel Scheduler Exploiting Static Data Dependencies

Run-time support for multi-level disjoint memory address spaces

Bigger Buffer k-d Trees on Multi-Many-Core Systems

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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