Search Results for "speedup"
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Showing 10 open source projects for "speedup"
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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XBNF Neurotranslator compiler
(X)BNF simple and clever translation grammar compiler
XBNF Neurotranslator is a powerfull extended BNF grammar language to handle translations easily and many features to handle different kind of situations. This project is for common arch binaries, C++ sources, tests & support tickets. No installation, juste get binary for your architecture : > See [Files] > binary.{version} Library of smart samples of grammars> https://sourceforge.net/projects/xbnf/ Docker image which embeds the Linux/64bits binary and the... -
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LightSpMV
lightweight GPU-based sparse matrix-vector multiplication (SpMV)
...We have evaluated LightSpMV using various sparse matrices and further compared it to the CSR-based SpMV subprograms in the state-of-the-art CUSP and cuSPARSE. Performance evaluation reveals that on a single Tesla K40c GPU, LightSpMV is superior to both CUSP and cuSPARSE, with a speedup of up to 2.60 and 2.63 over CUSP, and up to 1.93 and 1.79 over cuSPARSE for single and double precision, respectively. -
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libznz with zindex
This is a fast version of libznz used in NIfTI C library
This library provides notable speed-up for seeking in large compressed NIfTI files. It is compatible with the libznz interface which is part of NIfTI C library to handle multidimensional (mostly MRI) images. Only the replacement of the currently available libznz library is necessary. To reach the expected speed-up the compressed NIfTI data has to be indexed with the included tool. To compile the binaries, the sources available in this project have to be replaced with the ones in... -
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MICA-aligner
Next-generation sequencing short reads aligner based on Intel® MIC
...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). -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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EASEA (EAsy Specification of Evolutionary Algorithms: pronounce "easy") is a high-level language dedicated to the specification of evolutionary algorithms. EASEA up to version 0.7 compiles .ez specification files into C++ or JAVA object files.
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primeScaff is intended to automate the sometimes tedious process of manually designing specific primer pairs around gaps of genomic scaffolds and speedup the genome finishing stage of a genome sequencing project. It incorporates de-novo repeat finding using RECON to avoid as much as possible designing primers in repetitive regions and offers the possibility to easily fine-tune the primer design options using primer3. It outputs the repeat, gap and primer annotations in gff2 and gff3 to easily load and visualize the results using tools like UGENE (recommended) or artemis.
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Decision Table Preprocessor
Ccide is a Decision Table preprocessor.
.... */ // N - - - | speed == STOPPED // - - Y - | speed == SLOW // - N - Y | speed == FAST // ______|___________________ // X - X - | Stop(); // - - - X | SlowDown(); // - X - - | SpeedUp(); //END_TABLE: -
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COS - Parallel Community Detection
COS is a free, opensource parallel k-clique community detection method
Clique percolation method On Steroids (COS) enable k-clique communities to be detected, in parallel, from large-scale networks. Its low memory requirements, together with its linear speedup, make it really efficient, even on dense, highly interconnected networks. COS should be the method of choice for k-clique community detection aiming at very high performance and low resource requirements. -
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CUDA-SPHERE-FWD-MEEG
CUDA accelerated spherical model forward solution for EEG/MEG
...The 1-Sphere forward solution for the MEG and the 4-Sphere forward solution for the EEG is implemented in CUDA C and an accelerated solution is obtained using the NVIDIA GPU when the solution is calculated for a large number of dipoles (on the order of 15000 and above) and sensor locations. Speedup by a factor of 22 and 32 is obtained for the EEG and MEG solution respectively when compared to the fastest CPU implementation available in the public domain. The complete source code and pre-compiled binaries are also made available via an open source license (GPL Version 3). A CUDA enabled NVIDIA graphics card is required to use the software. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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MLboost: Machine Learning boost library in Python. MLboost main goal is to speedup any Machine Learning projects by simplifying data preprocessing, features selection and data visualisation. Design by Machine Learning practitioners to let them do ML...;)
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