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    6%
    Supercomputing Frontiers Europe 2020 Call for Papers
    22.01.2020
    Home »  News  » 
     
    -performance computing.” Candidates are invited to submit a 4-page extended abstract in PDF format through the EasyChair submission system.  See the conference website for more information.
    6%
    Creating Virtual SSDs
    21.01.2020
    Home »  Articles  » 
    and parity layout of a typical RAID 5 volume. If you were to do the math, you have four 7TB drives with one drive’s worth of capacity hosting the parity, so the RAID array will produce (7 × 4) - 7 = 21TB
    6%
    Profiling Python Code
    09.12.2019
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    |         0|            0|            0|  0.00%|# md test code      2|         0|            0|            0|  0.00%|      3|         2|  3.50475e-05|  1.75238e-05|  0.00%|import platform      4|         1|  2.19345e-05|  2.19345e-05|  0.00%|from
    6%
    Linux Writecache
    19.11.2019
    Home »  Articles  » 
    that is the issue. The purpose of a cache is to alleviate a lot of the burden for the drive to seek new sector locations for 4K, 8K, or other small I/O requests. Some of the more common caching methods or modes are
    6%
    Summit Tops Top500
    18.11.2019
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    .6 petaflops on the High Performance Linpack (HPL) benchmark. China took spots 3 and 4 with the Sunway TaihuLight system at 93 petaflops and the former champion Tianhe-2A at 61.4 petaflops. Overall
    6%
    Porting CUDA to HIP
    15.11.2019
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    using hipcc. 4. Correct any compiler errors or warnings and compile again. After a few build cycles, the ROCm executable will be ready to run. Hipify-clang Hipify-clang is a preprocessor that uses
    21%
    High-Performance Python 4
    14.10.2019
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    of classic HPC tools, such as MPI for Python (mpi4py ), a Python binding to the Message Passing Interface (MPI). Tools such as Dask focus on keeping code Pythonic, and other tools support the best performance ... High-Performance Python 4
    6%
    High-Performance Python 3
    19.09.2019
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     add_ufunc(x, y):     return x + y The decorator line defines the data types (i.e., int64  here) and the target for the decorator cuda . A simple test for the add_ufunc  Numba function is: a = np.array([1, 2, 3, 4]) b
    6%
    High-Performance Python 2
    22.08.2019
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    , which passes by value. Listing 1: Using ctypes from ctypes import byref, cdll, c_int   mult = cdll.LoadLibrary('./mult.so') add = cdll.LoadLibrary('./add.so') a = c_int(2) b = c_int(4) print
    6%
    High-Performance Python 1
    16.07.2019
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     +=x[i]     return total   x = numpy.arange(10_000_000); %time sum(x)   CPU times: user 145 ms, sys: 4.02 ms, total: 149 ms Wall time: 149 ms A speedup is nice to see, but believe it or not, quite a bit of the time

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