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Chenggang Zhao
2025-09-25 16:27:57 +08:00
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@@ -33,7 +33,7 @@ Despite its lightweight design, DeepGEMM's performance matches or exceeds expert
- [ ] Larger TMA multicast size for some shapes - [ ] Larger TMA multicast size for some shapes
- [x] MMA template refactor with CUTLASS - [x] MMA template refactor with CUTLASS
- [x] Remove shape limitations on N and K - [x] Remove shape limitations on N and K
- [ ] BF16 kernels - [x] BF16 kernels
- [ ] Split/stream-k optimizations - [ ] Split/stream-k optimizations
- [ ] Ampere kernels - [ ] Ampere kernels
- [ ] Polish docs - [ ] Polish docs