high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GTX 680

Parallel source code transformation techniques using design patterns

David del Rio Astorga

View

Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

View

Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers

Lukas Polok

View

Tags: Algorithms, Computer science, Differential equations, Factorization, FEM, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 680, OpenCL, Partial differential equations, PDEs, Sparse matrix, Tesla K40, Thesis

December 19, 2017 by hgpu

Methods for GPU Acceleration of Big Data Applications

Reza Mokhtari

View

Tags: big data, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan Black, Thesis

December 3, 2017 by hgpu

Accelerating Radio Astronomy with Auto-Tuning

Alessio Sclocco

View

Tags: AMD FirePro W9100, AMD Radeon R9 Fury X, Astrophysics, ATI, ATI Radeon HD 6970, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, OpenCL, Package, Tesla K20, Thesis

September 21, 2017 by hgpu

GPU Array Access Auto-Tuning

Nicolas Weber

View

Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 440, nVidia GeForce GT 620, nVidia GeForce GT 730, nVidia GeForce GTX 1080, nVidia GeForce GTX 480, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 590, nVidia GeForce GTX 680, nVidia GeForce GTX 780, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Performance, performance portability, Tesla C2070, Tesla K20, Thesis

August 8, 2017 by hgpu

Synthesis of Embedded Software using Dataflow Schedule Graphs

Abhay Raina

View

Tags: Computer science, DSP, nVidia, nVidia GeForce GTX 680, OpenCL, Signal processing, Thesis

July 2, 2017 by hgpu

Advanced Concurrency Control Algorithm Design and GPU System Support for High Performance In-Memory Data Management

Yuan Yuan

View

Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenCL, Thesis

February 7, 2017 by hgpu

Parallelization of BVH and BSP on the GPU

Martin Imre

View

Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 680, OpenCL, Raytracing, Rendering, Sorting, Thesis, Visualization

January 10, 2017 by hgpu

A Comparison of GPU Execution Time Prediction using Machine Learning and Analytical Modeling

Marcos Amaris, Raphael Y. de Camargo, Mohamed Dyab, Alfredo Goldman, Denis Trystram

View

Tags: Analytical model, Benchmarking, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 970, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X, nVidia Quadro K5200, Package, Performance, Tesla K20, Tesla K40

December 6, 2016 by hgpu

Optimization of Pattern Matching Algorithms for Multi- and Many-Core Platforms

Pedro Miguel Marques Pereira

View

Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, ATI Radeon HD 7970, Compression, Computer science, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 570 M, nVidia GeForce GTX 680, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan Black, nVidia Jetson TK1, OpenCL, OpenMP, Thesis

November 30, 2016 by hgpu

HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems

Prakalp Srivastava, Maria Kotsifakou, Vikram Adve

View

Tags: Code generation, Computer science, Data parallelism, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability

November 5, 2016 by hgpu

triSYCL: Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group

SYCL Code Generation for Multigrid Methods

PSST: A Tutorial for Parallelised Scalable Simulations in TensorFlow

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

MUSE: Miami University Simulation Environment

Temporospatial Epidemic Simulations Using Heterogeneous Computing

Pressure Point Analysis framework

Diagnosing Performance Bottlenecks in HPC Applications

Dynamic Distribution Pruning for Efficient Network Architecture Search

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

PyVisc

Classify QCD phase transition with deep learning

Bytespresso: a library for Java to C/CUDA translator based on deep reification

A Development Platform for Embedded Domain-Specific Languages

faimGraph

Breadth-First Search using Dynamic Parallelism on the GPU

Vulkan Multi-GPU application

Multi-GPU Rendering with Vulkan API

See all packages

* * *

* * *

2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

+

HGPU group © 2010-2019 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: