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    Module benchmarks/nvidia-geforce-gtx-1650/dasum

    Benchmark results for dasum on Nvidia Geforce Gtx 1650.

    n wgblas ms wgblas GB/s cuBLAS ms cuBLAS GB/s efficiency
    32 0.0226 0.0113 0.0181 0.0141 80.4%
    64 0.0226 0.0226 0.0180 0.0285 79.4%
    128 0.0225 0.0456 0.0180 0.0567 80.4%
    512 0.0229 0.1791 0.0180 0.2272 78.8%
    1024 0.0226 0.3631 0.0180 0.4563 79.6%
    4096 0.0228 1.4402 0.0181 1.8076 79.7%
    16384 0.0237 5.5277 0.0182 7.2049 76.7%
    65536 0.0265 19.7755 0.0182 28.8705 68.5%
    262144 0.0398 52.7029 0.0347 60.4297 87.2%
    1048576 0.0755 111.1251 0.0772 108.6833 102.2%
    4194304 0.2153 155.8757 0.2365 141.8816 109.9%
    16777216 0.7864 170.6736 0.8782 152.8314 111.7%

    Efficiency = wgblas GB/s ÷ cuBLAS GB/s × 100. 100% means parity with cuBLAS; values above 100% mean wgblas achieved greater throughput.

    dasum-default GB/s chart

    dasum-default ms chart

    • dasum.js — WebGPU benchmark script
    • dasum.c — CUDA / cuBLAS reference script

    Unless noted otherwise, every result above uses unit stride (incx = incy = 1) — the normal case, and the coalesced, best-case GPU access pattern. Real usage sometimes passes a non-unit stride (e.g. operating on a row or column of a larger matrix, where incx = lda), which breaks memory coalescing and costs measurably more. This section sweeps a few representative strides to characterize that cost separately, collapsed below by default — expand a stride to see its table and chart.

    Nvidia Geforce Gtx 1650 — stride = 4
    n wgblas ms wgblas GB/s cuBLAS ms cuBLAS GB/s efficiency
    32 0.0225 0.0114 0.0184 0.0139 81.8%
    64 0.0225 0.0228 0.0187 0.0274 83.2%
    128 0.0221 0.0464 0.0190 0.0538 86.2%
    512 0.0222 0.1843 0.0189 0.2166 85.1%
    1024 0.0220 0.3724 0.0180 0.4539 82.0%
    4096 0.0226 1.4484 0.0181 1.8140 79.8%
    16384 0.0254 5.1652 0.0187 7.0197 73.6%
    65536 0.0379 13.8262 0.0314 16.6843 82.9%
    262144 0.0716 29.2702 0.0642 32.6700 89.6%
    1048576 0.2061 40.6961 0.1993 42.0811 96.7%
    4194304 0.7434 45.1389 0.7347 45.6697 98.8%

    dasum-stride4 GB/s chart

    dasum-stride4 ms chart

    Nvidia Geforce Gtx 1650 — stride = 32
    n wgblas ms wgblas GB/s cuBLAS ms cuBLAS GB/s efficiency
    32 0.0228 0.0112 0.0182 0.0141 79.5%
    64 0.0228 0.0224 0.0181 0.0282 79.5%
    128 0.0225 0.0455 0.0180 0.0569 79.9%
    512 0.0228 0.1799 0.0173 0.2370 75.9%
    1024 0.0225 0.3642 0.0179 0.4580 79.5%
    4096 0.0279 1.1757 0.0179 1.8286 64.3%
    16384 0.0408 3.2151 0.0211 6.2249 51.6%
    65536 0.0743 7.0590 0.0393 13.3420 52.9%
    262144 0.2141 9.7939 0.1064 19.7071 49.7%

    dasum-stride32 GB/s chart

    dasum-stride32 ms chart

    Nvidia Geforce Gtx 1650 — stride = 256
    n wgblas ms wgblas GB/s cuBLAS ms cuBLAS GB/s efficiency
    512 0.0299 0.1371 0.0181 0.2261 60.6%
    1024 0.0303 0.2705 0.0175 0.4693 57.6%
    4096 0.0336 0.9743 0.0180 1.8253 53.4%
    16384 0.0508 2.5810 0.0221 5.9362 43.5%
    65536 0.1176 4.4594 0.0503 10.4257 42.8%

    dasum-stride256 GB/s chart

    dasum-stride256 ms chart

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