Search Results for "cuda" - Page 10
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Showing 309 open source projects for "cuda"
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Parallel Neural Networks
Neural networks in CUDA & OpenCL with back propagation algorithm
This project is my engineering diploma. It's aim is to compare the efficiency of both technologies and to check where which hacks works better. What is more one of my tasks is to compare different ways of decomposing computations in parallel. -
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Cryptohaze
GPU accelerated password cracking tools
A CUDA & OpenCL accelerated rainbow table implementation from the ground up, and a CUDA hash brute forcing tool with support for many hash types including MD5, SHA1, LM, NTLM, and lots more! -
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The sequence alignment task in MAGI (magi.ucsd.edu) is based on the miRanda algorithm, but we redesign the miRanda algorithm on GPU by taking its advantages of massively parallel computing and extra high memory bandwidth using using NVIDIA’s Compute Unified Device Architecture (CUDA). The CUDA-miRanda implementation is a fast microRNA target identification algorithm that aligns short nucleotide sequences (i.e., < 32 nucleotides) against longer reference sequences (e.g., 20k nucleotides). It has the ability to report multiple alignments and the corresponding traceback sequences for any given query-reference pair with up to 166x speedup on 4 GPUs compared to regular CPUs.
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MSA-CUDA: multiple sequence aligner
multiple sequence alignment on CUDA-enabled GPUs.
This project is not active any more since we failed to get the permit from the Clustal team to distribute our software. You can refer to the paper "Yongchao Liu, Bertil Schmidt, Douglas L Maskell:MSA-CUDA: multiple sequence alignment on graphics processing units with CUDA. ASAP 2009" for more details. -
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FASTCUDA
Execution of CUDA programs on a Heterogeneous FPGA-based Architecture
FASTCUDA methodology proposes the use of CUDA, a Graphical Processing Unit (GPU) language which exposes parallelism at source code in a FPGA hardware acceleration environment.The FASTCUDA toolset splits, with minimal user intervention, application's code into two parts: one that will be compiled and executed as parallel software in an embedded Multi-core, and another that will be synthesized and implemented with multiple special-purpose accelerators in hardware. -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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CUDA Fractal
Fractal Generator using CUDA technology.
This project will utilize CUDA technology to render Mandelbrot fractals with arbitrary frame buffer size, arbitrary precision numbers for infinite zoom capability, and have features to stress test CUDA based nVidia Titan GPUs. -
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HyCuda
Hybrid Framework Generator for CUDA
The HyCuda code-generator generates a template framework to easily compile different versions of a hybrid algorithm. When set up, you can switch between devices to execute part of the algorithm without having to worry about memory transfers. Based on a specification file that describes some properties of the algorithm, HyCuda generates C++11 header- and sourcefiles, only a few of which have to be modified by the programmer in order to implement the algorithm itself. The generated code... -
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grn_inference_multigpu
This project is a exhaustive search algoritm for multiple GPUs
...Among the search algorithms already proposed, there is the exhaustive search where the best feature subset is returned, although its computational complexity is unfeasible in almost all situations. The objective of this work is the development of a low cost parallel solution based on GPU architectures for exhaustive search with a viable cost-benefit. We use CUDA , a general purpose parallel programming platform that allows the usage of NVIDIA GPUs to solve complex problems in an efficient way. -
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FreDec
Parallelized FREquency DEComposer algorithm
...After selection of the initial frequency candidates, the algorithm passes through all their possible combinations and estimates their multi-frequency statistical significance. In the end, it prints out the set of largest frequency tuples that were still found significant. The GPU computing is implemented through CUDA and brings a significant performance increase. It is still possible to run FreDec solely on CPU, if no suitable GPU device is available in the system. See the details of the underlying theory in Baluev 2013, MNRAS, V. 436, P. 807 The description of the algorithm itself can be found in arXiv:1309.0100. Please go to the project forum to report any bugs. -
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Molecular Measurer
Parallel, Stochastic Measurement of Molecular Surface Area
A program to measure molecular surface area. The algorithm is GPU-accelerated using CUDA and is described in the research paper linked from the project home page. The input to the program is xyzr files, which can be generated from pdb files. -
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GPU Edge Detector
GPU Interactive Program For Edge detection
...(The code section is currently close until our article gets published) Commiting publicly to the project is currently closed but feel free to email us. The source code is dependant on CUDA 5.0 Samples now available as part of the CUDA Toolkit. "This software contains source code provided by NVIDIA Corporation." -
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iiitbaodv-gpu-aodv-IIIT Bangalore
AODV protocol for GPU
The project is implemented as part of the CS-110 operating system course at IIIT Bangalore 2013. Aodv protocol is implemented on GPU using CUDA 4.0. A significant gain in execution time is observed when compared to CPU. Thus a simulator which uses GPU can be built on similar lines of NS2 if all the protocols can be parallelized and implemented on GPU. Guide: Prof Shrisha Rao srao@iiitb.ac.in Prof Poonacha P G poonacha.pg@iiitb.ac.in Students: Abhilash C S abhilash.gowder@iiitb.org Abhishek Varshney abhishek.varshney@iiitb.org Dilip S dilip.s@iiitb.org Navik Yogesh Laljibhai navik.yogeshlaljibhai@iiitb.org Pradyot H Adavi pradyot.h.adavi@iiitb.org -
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aodv protocol on gpu IIIT Bangalore
Aodv protocol on gpu
The project is implemented as part of the CS-110 operating system course at IIIT Bangalore 2013. Aodv protocol is implemented on GPU using CUDA 4.0. A significant gain in execution time is observed when compared to CPU. Thus a simulator which uses GPU can be built on similar lines of NS2 if all the protocols can be parallelized and implemented on GPU. Guide: Prof Shrisha Rao srao@iiitb.ac.in Prof Poonacha P G poonacha.pg@iiitb.ac.in Students: Abhilash C S abhilash.gowder@iiitb.org Abhishek Varshney abhishek.varshney@iiitb.org Dilip S dilip.s@iiitb.org Navik Yogesh Laljibhai navik.yogeshlaljibhai@iiitb.org Pradyot H Adavi pradyot.h.adavi@iiitb.org -
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VolTK
A Lightweighted Toolkit For Volume Rendering
A Lightweighted Toolkit For Volume Rendering Project Started by Fei Yang Provide both CPU based and GPU (CUDA) based volume rendering. See http://www.fei-yang.org/?page_id=12 for complete descriptions and package downloads VolTK is a free C++ Toolkit that implements a tri-layer volume rendering framework, which is designed to meet the requirements of both algorithm research and application assemblage. Features * Tri-layer volume rendering * CPU implementation: multi-threading supported * GPU implementation: CUDA based implementation * CPU-GPU correspondance * Both low level interfaces and high level interfaces are provided * Replace the components easily with your own design * Various rendering modes: MIP/Isosurface/Full volume rendering * Endoscopy interaction mode implemented * Pre-integration supported * Cross platform (windows and RHEL tested) -
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Visual Simulation Laboratory
A framework for visual high-performance simulations
The Visual Simulation Laboratory is a framework for creating cross-platform high-performance physics-based simulations with a strong 3D graphical capability based upon Qt, OpenSceneGraph, CUDA and other technologies. -
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cuda template x64
cuda template (x64 windows7) cuda 5.0版本的模板
cuda 5.0版本的模板 x64 win7 vc2010 与nvidia整合到vc中的向导有所不同。 (1)向导只能生成win32程序，本模板则为x64 (2)向导没有连接SDK，本模板依存于SDK -
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GPUmat
GPUmat is a C/C++ GPU engine for Matlab based on NVIDIA CUDA.
Please download Windows and Linux version by clicking on "Browse All Files". GPUmat allows standard MATLAB code to run on GPUs. The engine is written in C/C++ and based on NVIDIA CUDA. Please contact gpyougroup@gmail.com for any questions. Unfortunately GPUmat was compiled for CUDA 5.0 and we basically stopped any support for other CUDA version because we don't have the resources to do it. But you can compile the source code if you want. Instructions to compile the source code: You need to download a SVN client and then from command line: svn export http://svn.code.sf.net/p/gpumat/code/trunk .... -
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birgHPCC
Rapid CUDA Cluster Deployment
...In short, birgHPCC is the world's first CUDA-ready, bioinformatics-based, live DVD. -
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Swift Sequence Alignment Program
GPU-based DNA sequence alignment program using Smith-Waterman
Swift is a DNA sequence alignment program that produces gapped alignment using the Smith-Waterman algorithm. It takes in a query file (FASTA format) and a reference file (FASTA format) as input. It outputs the reference name, read name, gapped alignment, alignment score, alignment start and end positions, and alignment length. I gave a talk on Swift in the GPU Technology Conference 2012. The talk can be accessed at... -
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DecGPU: CUDA-based Error Correction
The first distributed and parallel short-read error corrector on GPUs
DecGPU (Distributed Error correction on GPUs) is a parallel and distributed error correction algorithm for large-scale short read assembly. It is implemented using CUDA C++ and MPI, running on a GPU cluster. -
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CUSHAW: CUDA-based Short Read Alignment
the first aligner introducing a complete paired-end alignment on GPUs
CUSHAW is a CUDA compatible short read aligner to large genomes, such as the human genome, based on the Burrows Wheeler transform. -
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Fast flexible simulator of spiking neural networks. Easy to use graphical user interface with comprehensive monitoring facilities. Integrates with the NeMo CUDA simulator and the iSpike interface for the iCub robot.
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Vocale
Virtual CUDA Library Extension for Qemu
...Vocale is a software project written as part of a master thesis in GPU virtualization at the University of Oslo. Vocale is an extension to the Qemu hypervisor that emulates the CUDA library in Qemu's virtual machines. Applications in the virtual machines can thus use any GPU in the physical machine to accelerate their operation. Function calls and kernel launches are forwarded to the host and executed using the real CUDA library. We reference developers to the readme.pdf file in the source code for installation instructions. ...