Using Stream-K with hipBLASLt

hipBLASLt supports the Stream-K library, which reduces library sizes for a wide range of General Matrix-Matrix Multiplication (GEMM) shapes and sizes. It also provides more consistent performance, which might be better in some cases. Stream-K partitions an equal share of the aggregate inner-loop iterations among physical processing elements, which provides a near-perfect utilization of computing resources. For more information about Stream-K, see Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU on the arXiv website.

Configuring the kernel selection strategy

The TENSILE_SOLUTION_SELECTION_METHOD environment variable controls the hipBLASLt kernel selection strategy for GEMM operations. Set this variable to 2 to enable the Stream-K library or leave it set to 0 to continue to use the default settings.

• TENSILE_SOLUTION_SELECTION_METHOD=0 (Default)

  • Kernels are selected from the standard tuned libraries.

  • The heuristic best kernel is selected from the standard tuning grid.

  • User-driven tuning (tunable ops) only accesses kernels from the standard grid and free-size libraries.

  • This option does NOT use any Stream-K kernels.

• TENSILE_SOLUTION_SELECTION_METHOD=2 (Stream-K)

  • This enables the optional Stream-K library to use a GEMM scheduling algorithm that results in consistently good peak GEMM performance with far fewer tuned kernels.

  • The heuristic best kernel is selected from the Stream-K library.

  • User-driven tuning (tunable ops) considers all kernels from the standard grid, the free-size library, and the Stream-K library.

Configuring the kernel selection strategy

You can control The Stream-K kernel launch behavior using the environment variables listed in the following table. By default, Stream-K uses a model to predict the optimal grid size to use when launching a GEMM kernel at runtime. However, you can choose how many workgroups to launch a GEMM kernel with using the Stream-K settings below:

Environment Variable	Description
TENSILE_STREAMK_DYNAMIC_GRID	Set this variable to 3 to use the default setting. With this setting, the program automatically picks the number of work groups to launch for optimal performance. Set this variable to 0 to disable the dynamic grid and always use all available compute units.
TENSILE_STREAMK_FIXED_GRID	This variable overrides the default grid size and launches Stream-K GEMM kernels using the specified number of workgroups.
TENSILE_STREAMK_MAX_CUS	This variable sets the maximum number of compute units to use for Stream-K kernels. By default, Stream-K kernels are allowed to use all compute units on the device, but this setting lets you limit the number of units that can be used.

Recommendations for using Stream-K

Stream-K is especially advantageous in certain situations. Follow these guidelines when choosing a kernel selection strategy, based on your application and the desired performance.

• Wide range of GEMM sizes: Stream-K is a better choice for applications that handle a variety of GEMM shapes and sizes.

• Non-uniform dimensions: Stream-K is particularly beneficial for GEMMs where one dimension is significantly larger than the others.

• Consistent performance: Stream-K provides more consistent peak performance than the default selection method by evenly distributing work across the available compute units.

Managing Stream-K resource use

Follow these guidelines to optimize how Stream-K uses resources:

• Promoting concurrency: Use TENSILE_STREAMK_FIXED_GRID to limit the number of workgroups. This prevents GEMM from monopolizing the GPU resources and allows other kernels to run concurrently.

  The following example limits the GEMM kernels to 64 workgroups:

  export TENSILE_STREAMK_FIXED_GRID=64
  

• Limiting compute units: Use TENSILE_STREAMK_MAX_CUS to restrict the number of compute units the Stream-K kernels can use.

  This example limits the GEMM kernels to 32 compute units:

  export TENSILE_STREAMK_MAX_CUS=32