Jiajia Qin b30e721dc8 [js/webgpu] Provide a naive vectorized matmul algorithm (#18758) ... ### Description This PR provided a vectorized matmul algorithm. In most situations, we still go to the workgroup memory optimized matmul. But for some situations, like N and K are very small, using workgroup optimized matmul can't fully utilize the underlying hardware due to the 32x32 tile size. So for very small N/K, we switch to the naive vectorized matmul algorithm to improve the hardware execution unit usage. With this PR, matmul with input0: [1, 36864, 3], input1: [1, 3, 3], input2: [3] becomes less than 1 ms from 4.34 ms on Intel Gen9 GPUs.		2023-12-13 09:03:23 -08:00
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ops	[js/webgpu] Provide a naive vectorized matmul algorithm (#18758)	2023-12-13 09:03:23 -08:00
attribute-with-cache-key.ts	[js] optimize eslint config (#18460)	2023-11-20 12:00:56 -08:00
gpu-data-manager.ts	[js/webgpu] support using uniform buffer (#17803)	2023-10-10 00:31:12 -07:00
op-resolve-rules.ts	[JS/Web] Added uniforms to Reduce, Resize and Split Ops. (#18727)	2023-12-12 11:12:23 -08:00
program-manager.ts	[js/webgpu] allow to specify callback for profiling data (#18732)	2023-12-07 14:10:28 -08:00
types.ts	[js/webgpu] revise uniform support (#17871)	2023-10-11 16:41:46 -07:00