Showing 4 open source projects for "data visualization, research"

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  • 1
    coarrays

    coarrays

    A free Fortran 2008, 2018 coarrays course with notes and exercises

    Coarrays are native Fortran means for SPMD parallel programming. At runtime multiple copies of the executable (called images) are executing asynchronously. The F2008 standard provides coarray syntax, remote calls, coarray data objects, allocatable coarrays, syncronisation, atomics, etc. F2018 adds new functionality, e.g. collectives, teams, events, more atomics. Coarrays are used extensively in CASUP library for HPC (https://cgpack.sourceforge.io). The course is aimed at experienced...
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  • 2
    Database Benchmark
    ...We will continue to upload the source and binaries here, but the code repository will be innactive. Database Benchmark is one of the most powerfull open source tools designed to stress test database management systems with large data flows. The application performs two main test scenarios: - Insertion of large amount of randomly generated records with sequential or random keys; - Read of the inserted records, ordered by their keys. Database Benchmark can be used as an additional viewpoint when the research engineers or software architects asses the appropriate background storage engine for their mission critical systems. ...
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    Downloads: 0 This Week
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  • 3

    DDMTestbed

    An independent Data Distribution Management testbed

    DDMTestbed is a testbed that allows you to generate random instances and evaluate your proposed resolutive method measuring execution time, memory occupied and distance from the optimal solution. It has been written in C language.
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  • 4

    Java/C Comparative Benchmarks

    Java and C Comparative Performance Benchmarks

    A collection of software benchmarks developed to compare the performance of Java with C on identical code. No language libraries were used to avoid implementation differences. Some of the benchmarks are also implemented in Python and Scala. There are benchmarks for bit twiddling, numerical computing, data structure manipulation, concurrent computing, callouts to native libraries, and, graphics processing units (GPU) utilization.
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