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Parallelize compute_chunks_operands #294
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Looks like the |
Hey @sragss I'm open to taking on this issue. |
Please! Feel free to ask questions here. |
@githubsands any update on this? |
Is this issue still open? |
Hi @sragss, I'm having difficulty in reproducing the 1.8% (I'm only getting .09%). Right now I run
and then getting stats in perfetto using SQL
So this is only getting .09%. I'm running on an 8-core M1 with 242 cycle count. So I had a couple of questions:
|
242 cycle count is too small to get an idea of relative asymptotic performance. Can you try a bigger example – maybe in the 128k-512k range? Also thought sha2-chain was around 3M cycles, did you modify it to fit in a smaller amount of RAM? |
Sorry, silly mistake. When I looked up how to find the cycle count, the thing it actually pointed me to was the battery cycle count 🤦 I didn't realize you were referring to trace length - 3,632,556 in my case. I ran it on the original sha2-chain. But re: the questions above, what are the steps for reproducing your benchmarks? Or possibly do you have any ideas why I might be getting a much lower %? |
Hi , can i take on this issue ? |
For a 64 core machine at a cycle count of ~16M, Jolt spends ~1.8% of its time in a segment called compute_chunks_opreands here.
This segment allocates and computes
C
different chunks for each instruction. For example for the EQ instruction we split the input operands X, Y into 4 8-bit chunks (WORD_SIZE / C). We then can compute EQ over each chunk individually.Idea for acceleration: Split
chunks_x
andchunks_y
into mutable slices. Iterate over each and compute the values in parallel writing the the slice indexes directly.It may be helpful to review the tracing strategy for performance testing.
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