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GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

Performance Portable GPU Code Generation for Matrix Multiplication

Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan Black, OpenCL

February 10, 2016 by hgpu

Programming GPUs with C++14 and Just-In-Time Compilation

Michael Haidl, Bastian Hagedorn, Sergei Gorlatch

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Tags: Compilers, Computer science, CUDA, Intel Xeon Phi, LLVM, nVidia, OpenCL, PTX, Tesla K20

February 10, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

Portable Programming Models for Heterogeneous Platforms

Deepak Majeti

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7660, Computer science, Heterogeneous systems, nVidia, OpenCL, Package, Tesla M2050, Thesis

February 8, 2016 by hgpu

High performance high-order numerical methods: applications in ocean modeling

Rajesh Gandham

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Tags: ATI, ATI Radeon HD 7970, cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX Titan, OCCA, OpenCL, OpenMP, real time systems, Tesla C2050, Tesla K20, Tesla K40, Thesis

February 8, 2016 by hgpu

Workload distribution and balancing in FPGAs and CPUs with OpenCL and TBB

Rafael Asenjo, Angeles Navarro, Andres Rodriguez, Jose Nunez-Yanez

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia Quadro K600, OpenCL

February 8, 2016 by hgpu

Collaborative design and optimization using Collective Knowledge

Anton Lokhmotov, Grigori Fursin

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Tags: Computer science, Machine learning, OpenCL

February 8, 2016 by hgpu

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Jinhwan Park, Wonyong Sung

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Tags: Computer science, Deep learning, FPGA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan Black

February 6, 2016 by hgpu

Asynchronous Methods for Deep Reinforcement Learning

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

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Tags: Algorithms, Computer science, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla K40

February 6, 2016 by hgpu

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Gang Mei, Hong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Package

February 6, 2016 by hgpu

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

Milan Ceska, Petr Pilar, Nicola Paoletti, Lubos Brim, Marta Kwiatkowska

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Probabilistic model checking

February 6, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Performance Portable GPU Code Generation for Matrix Multiplication

Programming GPUs with C++14 and Just-In-Time Compilation

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Portable Programming Models for Heterogeneous Platforms

High performance high-order numerical methods: applications in ocean modeling

Workload distribution and balancing in FPGAs and CPUs with OpenCL and TBB

Collaborative design and optimization using Collective Knowledge

FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only

Asynchronous Methods for Deep Reinforcement Learning

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic 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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