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Viewing profile — gregjm

gregjm

HN member
Joined
Sat, Jun 03, 2023, 12:36 AM UTC
HN karma
21
Public activity
9 items

About gregjm

I work on GPU performance and optimization at Waymo.

Recent public activity

  1. comment
    Comment #47388944

    > I don't know JAX well enough to explain exactly why it's 3x faster than NumPy on the same matrix multiplications. JAX is basically a frontend for the XLA compiler, as you note. T…

  2. comment
    Comment #46729185

    I wonder why H100 H2D and D2H unpinned memcpy bandwidth is *faster* on PCIe with vendor B than on SXM with vendor D. Is resizable BAR available on PCIe but not SXM? Or, could it be…

  3. comment
    Comment #43931249

    TCMalloc never munmaps, instead it mmap(MAP_FIXED) within unpopulated PROT_NONE regions, and then madvise(MADV_FREE) at page granularity to reduce RSS. Perhaps a similar approach f…

  4. comment
    Comment #41095366

    Re: serpentine traversal, this has to do with the .reuse suffix applied to register operands as mentioned in your link. We don’t really have control over it because it’s happening …

  5. comment
    Comment #38031303

    The CUDA Runtime and Driver APIs have per-thread state, so using threads would unfortunately bypass our trick here to set the flag. Assuming you're on Linux, I might suggest creati…

  6. comment
    Comment #38027407

    > My so-called CPU “active” time is actually an inferred value; CUDA spins the CPU 100% constantly, even when the CPU is just waiting for the GPU The CUDA Runtime and Driver APIs a…

  7. comment
    Comment #37765941

    CUTLASS, which is NVIDIA’s C++ template library for writing matrix multiply and convolution kernels parametrized over input/output types, operators, and algorithm block sizes, theo…

  8. comment
    Comment #36199625

    > Interesting, anything in particular to read about it from Anandtech or Chips+Cheese or what? Or should I just read the whitepapers? (/sigh, reading primary sources, my only weakn…

  9. comment
    Comment #36172661

    Personally I would lean away from radix sort once the problem size gets too large, on Turing they made the per-SM L1 cache coherent and on Ampere they made L2 cache significantly l…