4.2 KiB
4.2 KiB
Hardware Capabilities
Interpretation Rule
Blackwell-native means using target-specific scheduling, state ownership, or
data movement supported by the target. Compiling the SM89 algorithm for
sm_121a is not sufficient. SM100, SM120, and SM121 are separate schedules.
Capability Matrix
| Capability | SM100 / B200 | SM120 / RTX PRO 6000 | SM121 / GB10 |
|---|---|---|---|
| Compute capability | 10.0 | 12.0 | 12.1 |
| TMA global/shared tensor copies | Documented | Documented | Documented |
| Native matrix programming model | tcgen05.mma with TMEM |
warp-level mma.sync |
SM120-family warp-level mma.sync |
| Programmer-visible TMEM | Yes | Not exposed | Not exposed |
| Native UMMA/tcgen05 INT8 | Exposed | Not exposed | Not exposed |
| Sage-compatible INT8 path | tcgen05.kind::i8 can be investigated |
supported classic INT8 mma.sync |
supported classic INT8 mma.sync |
| FP8 PV path | tcgen05 FP8 forms |
warp-level FP8 mma.sync |
warp-level FP8 mma.sync |
| Hopper WGMMA | Not a portable target path | Not a target path | CUDA 13 rejects for sm_121a |
| Registers per SM / max per thread | 64K / 255 | 64K / 255 | 64K / 255 |
| Shared memory per SM / block | 228 / 227 KiB | 100 / 99 KiB | 100 / 99 KiB |
| Maximum resident warps | 64 | 48 | 48 |
| Maximum resident threads | 2048 | 1536 | 1536 |
The public SM120/121 Blackwell MMA interface does not expose a Sage2-compatible
UMMA INT8 QK operation. Exact SM121 work therefore retains the supported INT8
mma.sync arithmetic while making TMA staging, role scheduling, ownership, and
handoff Blackwell-specific. FP8/FP4 QK would define vortex_fast, not
vortex_exact.
SM121 Facts From Retained Evidence
- The current 128-thread kernel uses 255 registers/thread and 32 KiB dynamic shared memory.
- Two CTAs provide eight resident warps, approximately
16.67%of the 48-warp ceiling. - A 168-register cap reaches three CTAs but spills catastrophically; compiler lifetime cleanup is not enough.
- CUDA 13 rejects
wgmma.fence,wgmma.mma_async,wgmma.commit_group, andwgmma.wait_groupforsm_121a. - CUTLASS 4.6 executes SM120-family block-scaled narrow MMA on GB10, but that is not an exact replacement for Sage2 INT8 QK.
- TMA producer/consumer scheduling is available, but role-specific register redistribution, named-barrier cost, and concurrent INT8/FP8 issue must be measured rather than assumed.
Required Capability Probes Before Prototype
- Compile empty 256- and 320-thread role-specialized CTAs; record per-thread registers, aggregate register allocation, occupancy, and spill traffic.
- Test whether any supported register-allocation control can give producer and consumer warps different practical budgets on SM121.
- Measure one-CTA residency at 48, 52, 56, 64, and 68 KiB shared memory.
- Measure TMA plus named-barrier two-slot handoff for 4 and 8 KiB payloads.
- Verify simultaneous INT8 QK and FP8 PV warps produce actual overlap in pipe counters, not merely alternating aggregate utilization.
- Establish thread-block cluster/DSM support, placement, simultaneous residency, and remote shared-memory latency on GB10 before accepting VEA-C.
- Record sustained clocks and power behavior for 128-, 256-, and 320-thread CTAs.
Portability Policy
- SM121 is first and must compile specifically for
sm_121a. - SM120 may share API and contract code, but requires its own geometry, resource model, profiler evidence, and acceptance gates.
- SM100 should use a separate
tcgen05/TMEM design. Porting the SM121 register accumulator schedule unchanged would discard the principal SM100 capability.
Sources
- NVIDIA CUDA GPU Compute Capability: https://developer.nvidia.com/cuda-gpus
- CUDA Programming Guide: https://docs.nvidia.com/cuda/cuda-programming-guide/
- PTX ISA: https://docs.nvidia.com/cuda/parallel-thread-execution/
- CUTLASS Blackwell functionality: https://github.com/NVIDIA/cutlass/blob/main/media/docs/cpp/blackwell_functionality.md
- CUTLASS
mma_sm100_umma.hpp,mma_sm120.hpp, and architecture config. - Local measurements:
SAGE2_BLACKWELL_DESIGN.mdand retained P0/post-FC2 NCU reports.
Documented API support does not establish throughput, latency, queue depth, or bit identity. Those remain target-specific measurements.