
DOCS . JAX . DEV {
}
Title:
Matrix Multiplication โ JAX documentation
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4 years and 8 months (reg. 2020-11-01).
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Keywords {๐}
matmul, flops, matrix, multiplication, time, int, utilization, def, memory, jnpbfloat, compute, analyzematmul, kernel, tpu, pallas, block, lambda, bandwidth, bound, usage, performance, return, transposerhs, inputs, transpose, activation, pipelining, function, blocks, jax, import, size, support, bigger, zref, jaxarray, accref, mmflops, activationactivation, operations, lets, output, dimension, yref, point, dtypedtype, transposerhstrue, xshape, plwhenplprogramid, efficient,
Topics {โ๏ธ}
jit-ted jax computation ๐ฅ ๐ ๐ int8 matrix multiplication matrix multiplication performance ๐ ๐ ] block matrix multiplication compilation exporting actual runtime smaller matrix multiplications matrix multiplication fused blocked matrix multiplication ๐ ๐ pallas import tpu matrix multiplication routine hand-side transpose matrix multiplication kernel fast matrix multiplication size matrix multiplications candidate block sizes reasonable block sizes bigger matrix multiplications total matrix multiplication experimental import pallas memory bandwidth measures memory bandwidth usage memory usage applies bfloat16 matrix multiplication reach xla performance simple neural network modern deep learning floating point operations ๏ฟฝfloating point operationsโ ๏ฟฝfloating point operations basic calculation tells allocating scratch spaces allocate scratch spaces simple timing experiments measuring python dispatch scalar prefetch float32 matrix multiplication fused activation functions multiples small matrices add bf16 support input bf16 matrices block sizes bm memory transfers happening total bandwidth usage ๐ ๐ minimum bandwidth usage instance tpu v5e
Questions {โ}
- How could we do it?
- However, what if our inputs and outputs were bigger instead?
- What do we mean by that?
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