Search Results for "gpu speed" - Page 4
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Showing 91 open source projects for "gpu speed"
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CUDA-MEME
Ultrafast scalable motif discovery algorithm using GPU computing
mCUDA-MEME is a well-established ultrafast scalable motif discovery algorithm based on MEME (version 4.4.0) algorithm for multiple GPUs using a hybrid combination of CUDA, MPI and OpenMP parallel programming models. This algorithm is a further extension of CUDA-MEME (based on MEME version 3.5.4) with respect to accuracy and speed and has been tested on a GPU cluster with eight compute nodes and two Fermi-based Tesla S2050 (and Tesla-based Tesla S1070) quad-GPU computing systems, running the Linux OS with the MPICH2 library. The experimental results showed that our algorithm scales well with respect to both dataset sizes and the number of GPUs. At present, OOPS and ZOOPS models are supported, which are sufficient for most motif discovery applications. ... -
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SW#
smith waterman, gpu, cuda, alignment
SW# (swsharp) is a library for sequence alignment based on CUDA enabled GPUs. It utilizes Hirschbergs and Ukkonens algorithm for memory efficiency and additional speed up. The library is scalable for use with multiple GPUs. Some parts of the library utilize MPI for CUDA enabled clusters. This work has been supported in part by Croatian Science Foundation under the project UIP-11-2013-7353. Database search now... -
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openAviToGif
Video to GIF converter
With openAviToGif you can convert video into an animated gif file. What's new in v0.6: -GPU processing -improvements and bugfixes Use your video card to speed up the conversion. Recommended for those who have high-end video cards. -
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Functy
Functy is a 3D graph drawing package that generates immediate results.
Note: The Functy and libsymbolic code has now moved to GitLab: https://gitlab.com/flypig/functy https://gitlab.com/flypig/libsymbolic This project has been left here for archival purposes. Functy is a 3D graph drawing package. It allows Cartesian, spherical and parametric curve functions to be plotted and altered quickly and easily. This immediacy and the vivid results promote fun exploration of 3D function. Rendering is performed entirely on the GPU to improve visuals and... -
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NCrypted
NCrypted - simple file encryption software with GPU support
NCrypted is a simple file encryption software. It is implemented on top of AES 128-bit algorithm. This software was designed to be as simple as possible, so you won't find any fancy, flashy gui elements or complicated advanced options. You can also use it directly from command line. NCrypted can automatically detect and take advantage of your NVidia graphics card to speed up the file processing. -
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SOAP3-DP
Fast, Accurate and Sensitive GPU-based Short Read Aligner
Latest Code on GitHub: https://github.com/aquaskyline/SOAP3-dp SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners BarraCUDA and CUSHAW, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. ... -
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MICA-aligner
Next-generation sequencing short reads aligner based on Intel® MIC
Latest Code in GitHub: https://github.com/aquaskyline/MICA-aligner To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC’s limitation and the extra parallelism inside each MIC core. Experiments on aligning 150bp paired-end reads show that MICA using one MIC board is ~4.85 times faster than the CPU-(multi-core)-based BWA-MEM and about the same speed as the GPU-based SOAP3-dp. Furthermore, MICA’s simplicity allows very efficient scale-up when multiple MIC boards are used in a node (3 cards gives a 14-fold speedup over 6-core BWA-MEM). -
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Ogljpeg
JPEG decompression accelerated on GPU using OpenGL compute shaders
Ogljpeg is JPEG decompression library. Primary focus of this project is to speed up texture upload process using JPEG compression. For this purpose OpenGL compute shaders are used and result of decompression is OpenGL texture loaded in GPU memory. -
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Open Broadcaster Software
Free, open source software for live streaming and recording
NOTE: Please visit our website for downloads. Open Broadcaster Software is free and open source software for the purposes of streaming live media content to the internet or to video files. Originally I made Open Broadcaster Software for myself because I thought "why should I pay a subscription for software I could probably write?" And so, I did and Open Broadcaster Software evolved into somewhat of a monster of a project. That's pretty much the Open Broadcaster Software story. I hope... -
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gpgmpi
An GPGMP in C++/OpenCL with improved time step algorithm
The need for speed is always a challenge for large-scale stochastic simulation. This software is based on a parallel stochastic simulation algorithm already implemented on Graphics Processing Unit (GPU) for inhomogeneous reaction-drift-diffusion systems. We suggest an improved choice of time step which turns out to be almost 3 or even more times fast with nearly identical accuracy. -
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GPU Edge Detector
GPU Interactive Program For Edge detection
This is a standalone, all in one Edge Detector that is mainly running on GPU using the CUDA technology. Each algorithm also has an OpenCV counterpart which is the closest one in terms of speed. This project is partly based on the Sobel Filter sample available as part of the CUDA SDK. This project also partly uses the OpenCV library to load different types of image. This program includes: Prewitt filter, Roberts Cross, Canny edge detector and finally Sobel filter with the ability to modify some variables such as sigma in Canny and see the result in real-time. ... -
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overdrive5
AMD GPU power/fan control via ADL OverDrive5 interface
Command line tool that uses AMD ADL OverDrive5 interface to control power and fan speed of their GPU boards. Look at the Wiki page about usage. Works on Windows and Linux (if video driver is loaded). -
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autofan is a very simple and light daemon written in C that automatically controls fan speed, according to GPU/board temperature, on an Nvidia-card that doesn't support automatic hardware control. It builds ontop of the nvclock backend.
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gpt-oss-120b
OpenAI’s open-weight 120B model optimized for reasoning and tooling
GPT-OSS-120B is a powerful open-weight language model by OpenAI, optimized for high-level reasoning, tool use, and agentic tasks. With 117B total parameters and 5.1B active parameters, it’s designed to fit on a single H100 GPU using native MXFP4 quantization. The model supports fine-tuning, chain-of-thought reasoning, and structured outputs, making it ideal for complex workflows. It operates in OpenAI’s Harmony response format and can be deployed via Transformers, vLLM, Ollama, LM Studio, and PyTorch. Developers can control the reasoning level (low, medium, high) to balance speed and depth depending on the task. ... -
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DiffusionGemma
NVFP4 DiffusionGemma model for fast multimodal text generation
...Built on the Gemma 4 26B A4B Mixture-of-Experts architecture, it has 25.2B total parameters and 3.8B active parameters, balancing capability with efficient inference. Its diffusion-based generation produces tokens in parallel 256-token blocks, enabling very high-speed output, with reported generation above 1,100 tokens per second on NVIDIA Hopper H100 in FP8. The model supports a 256K-token context window, configurable thinking mode, native function calling, structured JSON output, and multilingual inference across 35+ languages. The NVFP4 quantization reduces weights and activations from 16-bit to 4-bit, lowering disk size and GPU memory needs for vLLM deployment. -
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granite-timeseries-ttm-r2
Tiny pre-trained IBM model for multivariate time series forecasting
granite-timeseries-ttm-r2 is part of IBM’s TinyTimeMixers (TTM) series—compact, pre-trained models for multivariate time series forecasting. Unlike massive foundation models, TTM models are designed to be lightweight yet powerful, with only ~805K parameters, enabling high performance even on CPU or single-GPU machines. The r2 version is pre-trained on ~700M samples (r2.1 expands to ~1B), delivering up to 15% better accuracy than the r1 version. TTM supports both zero-shot and fine-tuned...