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In computing, CUDA (Compute Unified Device Architecture) is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
Uses fast SIMD instructions (SSE) to accelerate alignment calculations on CPU. If available, alignments are computed on GPU (using OpenCL/CUDA) further reducing runtime 20-50%. Yes No Yes Yes, POSIX Threads, OpenCL/CUDA, SSE Free [48] 2013 Omixon Variant Toolkit Includes highly sensitive and highly accurate tools for detecting SNPs and indels.
CUDA is a parallel computing platform and programming model that higher level languages can use to exploit parallelism. In CUDA, the kernel is executed with the aid of threads. The thread is an abstract entity that represents the execution of the kernel. A kernel is a function that compiles to run on a special device. Multi threaded ...
This is a list of commands from the GNU Core Utilities for Unix environments. These commands can be found on Unix operating systems and most Unix-like operating systems. GNU Core Utilities include basic file, shell and text manipulation utilities. Coreutils includes all of the basic command-line tools that are expected in a POSIX system.
Multiple categories on one line Against convention Two or more categories on one line. High: No Square brackets with no correct end Display problem Found a "[[" brackets with no corresponding "]]" brackets. Off: Yes HTML named entities Source readability HTML code found where a Unicode character could be used.
Arm MAP, a performance profiler supporting Linux platforms.; AppDynamics, an application performance management solution [buzzword] for C/C++ applications via SDK.; AQtime Pro, a performance profiler and memory allocation debugger that can be integrated into Microsoft Visual Studio, and Embarcadero RAD Studio, or can run as a stand-alone application.
OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]