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CUDA


Технология вычислений на графических процессорах Nvidia

  • Введение
  • Аппаратное обеспечение
  • Программное обеспечение
  • Производительность
  • Пример для Maple
  • Источники информации

  • Введение

    CUDA Logo

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    Аппаратное обеспечение

    См. статья о Nvidia Tesla в Wikipedia
    TECHNICAL SPECIFICATIONS
    GTX TITAN Black GTX TITAN Black на i7h ( Micro-architecture Kepler GK110 )
    Tesla C2075 Tesla C2075 на Theor2 ( Micro-architecture Fermi GF100 )
    • FORM FACTOR → 9.75. PCIe x16 form factor
    • # OF CUDA CORES → 448
    • FREQUENCY OF CUDA CORES → 1.15 GHz
    • DOUBLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 515 Gflops
    • SINGLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 1.03 Tflops
    • TOTAL DEDICATED MEMORY → 6GB GDDR5*
    • MEMORY SPEED → 1.5 GHz
    • MEMORY INTERFACE → 384-bit
    • MEMORY BANDWIDTH → 144 GB/sec
    • POWER CONSUMPTION → 225W TDP
    • SYSTEM INTERFACE → PCIe x16 Gen2
    • THERMAL SOLUTION → Active Fansink
    • DISPLAY SUPPORT → Dual-Link DVI-I: 1 → Maximum Display Resolution 1600x1200
    GeForce GTX-680 GeForce GTX-680 на i7A и i7X ( Micro-architecture Kepler GK104 )
    • # OF CUDA CORES (Unified Shaders) → 1536
    • FREQUENCY OF CUDA CORES → 1.006 GHz
    • DOUBLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 128.77 Gflops
    • SINGLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 3090.43 Gflops
    • TOTAL DEDICATED MEMORY → 2GB GDDR5*
    • MEMORY SPEED → 1.5 GHz
    • MEMORY INTERFACE → 256-bit
    • MEMORY BANDWIDTH → 192 GB/sec
    • POWER CONSUMPTION → 195 W TDP
    • SYSTEM INTERFACE → PCIe x16 Gen3
    • THERMAL SOLUTION → Active Fansink
    • DISPLAY SUPPORT → Dual-Link DVI-I: 1 → Maximum Display Resolution 1600x1200
    GTX-470 image GeForce GTX-470 на i7b ( Micro-architecture Fermi GF100 )
    • # OF CUDA CORES → 448
    • FREQUENCY OF CUDA CORES → 1.215 GHz
    • DOUBLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 136.08 Gflops
    • SINGLE PRECISION FLOATING POINT PERFORMANCE (PEAK) → 1088.64 Gflops
    • TOTAL DEDICATED MEMORY → 1.28 GB GDDR5
    • MEMORY SPEED → 1.5 GHz
    • MEMORY INTERFACE → 320-bit
    • MEMORY BANDWIDTH → 133.9 GB/sec
    • POWER CONSUMPTION → 215 W TDP
    • SYSTEM INTERFACE → PCIe x16 Gen2
    • THERMAL SOLUTION → Active Fansink
    • DISPLAY SUPPORT → Dual-Link DVI-I: 1 → Maximum Display Resolution 1600x1200


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    Программное обеспечение



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    Производительность

    for (i = 0; i < MatrixSize; i++)
    for (j = 0; j < MatrixSize; j++)
    for (k = 0; k < MatrixSize; k++)
    C[j][i] += A[j][k] * B[k][i];
    GFlops = 2 * MatrixSize3 /109/ExecutionTime

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    Пример для Maple

    theor2:> maple test_cuda.mpl
    |\^/| Maple 16 (X86 64 LINUX)
    ._|\| |/|_. Copyright (c) Maplesoft, a division of Waterloo Maple Inc. 2012
    \ MAPLE / All rights reserved. Maple is a trademark of
    <____ ____> Waterloo Maple Inc.
    | Type ? for help.
    > CUDA:-IsEnabled();
    false

    > CUDA:-Enable(true);
    false

    > CUDA:-IsEnabled();
    true

    >
    > CUDA:-HasDoubleSupport();
    table([0 = true])

    >
    > with(LinearAlgebra):
    > M:=RandomMatrix(4000,outputoptions=[datatype=float[4]]);
    [ 4000 x 4000 Matrix ]
    M := [ Data Type: float[4] ]
    [ Storage: rectangular ]
    [ Order: Fortran_order ]

    > N:=RandomMatrix(4000,outputoptions=[datatype=float[4]]);
    memory used=124.1MB, alloc=126.0MB, time=0.88
    [ 4000 x 4000 Matrix ]
    N := [ Data Type: float[4] ]
    [ Storage: rectangular ]
    [ Order: Fortran_order ]

    >
    > time[real](MatrixMatrixMultiply(M,N));
    memory used=185.2MB, alloc=187.1MB, time=0.92
    0.617

    > CUDA:-Enable(false);
    true

    > time[real](MatrixMatrixMultiply(M,N));
    5.623

    >
    >
    > CUDA:-Enable(true);
    false

    > M:=RandomMatrix(4000,outputoptions=[datatype=float[8]]);
    memory used=368.4MB, alloc=248.1MB, time=7.48
    [ 4000 x 4000 Matrix ]
    M := [ Data Type: float[8] ]
    [ Storage: rectangular ]
    [ Order: Fortran_order ]

    > N:=RandomMatrix(4000,outputoptions=[datatype=float[8]]);
    memory used=490.6MB, alloc=370.2MB, time=7.88
    [ 4000 x 4000 Matrix ]
    N := [ Data Type: float[8] ]
    [ Storage: rectangular ]
    [ Order: Fortran_order ]

    >
    > time[real](MatrixMatrixMultiply(M,N));
    1.640

    >
    > CUDA:-Enable(false);
    true

    >
    > time[real](MatrixMatrixMultiply(M,N));
    10.614

    >
    > CUDA:-Properties();
    [table(["Max Threads Dimensions" = [1024, 1024, 64], "Clock Rate" = 1147000,

    "Max Grid Size" = [65535, 65535, 65535], "Memory Pitch" = 2147483647,

    "Max Threads Per Block" = 1024, "Warp Size" = 32,

    "Kernel Exec Timeout Enabled" = false, "Resisters Per Block" = 32768,

    "ID" = 0, "Texture Alignment" = 512, "Minor" = 0,

    "MultiProcessor Count" = 14, "Shared Memory Per Block" = 49152,

    "Total Global Memory" = 4294967295, "Major" = 2, "Name" = "Tesla C2075",

    "Total Constant Memory" = 65536,

    "Device Overlap" = 1

    ])]

    > quit
    memory used=734.8MB, alloc=614.3MB, time=20.10

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    Источники информации



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