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v3.9 update (#2203)

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* v3.9 update

* voidD

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Co-authored-by: yuzhai <yuzhai@nvidia.com>
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Yujia Zhai
2025-04-02 12:11:18 -07:00
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# Dependent kernel launches
The Hopper and Blackwell architectures supports a new feature through which two kernels in the same stream can
overlap their execution, named
[Programmatic Dependent Launch (PDL)](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization).
This allows kernels with conflict in global memory to programmatically and safely overlap portions
of their execution. Primary kernel can signal it is about to finish execution, and the next kernel is expected to
programatically wait on the previous kernel to finish flushing its memory.
We enable PDL by setting a flag through the extended CUDA launch APIs. All CUTLASS kernels with PDL support
will wait on the prior kernel to flush its output to memory and signal the next kernel to start. This means
they can safely be dropped in with any other set of kernels using PDL as long as they also adhear to waiting on
the prior to flush its memory as well.
For more information, we refer you to the [PDL section in the CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#programmatic-dependent-launch-and-synchronization).
## Using dependent launch in CUTLASS
When building CUTLASS, you can use the `CUTLASS_ENABLE_GDC_FOR_SM90` and `CUTLASS_ENABLE_GDC_FOR_SM100` macro
respectively to enable PDL-related instructions:
```
cmake . -DCUTLASS_ENABLE_GDC_FOR_SM90=1
```
Note that this only adds PDL-related instructions to the _kernels_, but to actually allow a dependent
launch, you must also run your GEMM kernel with PDL:
```
gemm.run(
/* stream = */ stream,
/* cuda_adapter = */ nullptr,
/* launch_with_pdl = */ true
);_
```
## Model-Aware Optimizations with PDL
In [example 63](https://github.com/NVIDIA/cutlass/tree/main/examples/63_hopper_gemm_with_weight_prefetch/README.md), we use PDL to explicitly optimize for
performance of kernels where we know that one of the input matricies (our weights) will not be produced by a prior
kernel. In that case, we only need to wait on the prior kernels memory flush in order to load the other input matrix
(our activations). During our prologue, we can prefetch our weights to improve performance for memory bandwidth-bound
problem sizes. For more informations we refer the reader to [the example](https://github.com/NVIDIA/cutlass/tree/main/examples/63_hopper_gemm_with_weight_prefetch/README.md).
## Copyright
Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
```
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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