Using logging and heuristics

This topic contains information about debugging and improving the application performance when using the hipBLASLt APIs.

Logging

You can enable the hipBLASLt logging mechanism by setting the following environment variables before launching the target application:

• HIPBLASLT_LOG_LEVEL=<level>: The level can be one of the following values:

  Value	Setting	Description
  0	Off	Logging is disabled (default)
  1	Error	Only errors are logged
  2	Trace	API calls that launch HIP kernels log their parameters and important information
  3	Hints	Hints that can potentially improve the application’s performance
  4	Info	Provides general information about the library execution and can contain details about the heuristic status
  5	API trace	API calls log their parameters and important information

• HIPBLASLT_LOG_MASK=<mask>: The mask is a combination of the following flags:

  Value	Description
  0	Off
  1	Error
  2	Trace
  4	Hints
  8	Info
  16	API trace
  32	Bench
  64	Profile
  128	Extended profile

  The levels are cumulative: level 4 also enables errors, trace and hints, and level 5 enables everything above it. HIPBLASLT_LOG_MASK is only consulted when HIPBLASLT_LOG_LEVEL is unset, and unlike a level it selects exactly the flags you list.

• HIPBLASLT_LOG_FILE=<file_name>: The file_name is a path to a logging file. The file name can contain %i, which is replaced with the process ID, for example, <file_name>_%i.log. If HIPBLASLT_LOG_FILE is not defined, the log messages are printed to stderr. This variable has no effect on its own: the log file is opened only when HIPBLASLT_LOG_LEVEL or HIPBLASLT_LOG_MASK has enabled logging.

• HIPBLASLT_ENABLE_MARKER=1: Setting HIPBLASLT_ENABLE_MARKER to 1 enables marker trace for ROCProfiler profiling.

Heuristics cache

hipBLASLt uses heuristics to pick the most suitable matmul kernel for execution based on the problem sizes, GPU configuration, and other parameters. This requires performing some computations on the host CPU, which could take tens of microseconds. To overcome this overhead, it’s recommended that you query the heuristics once using hipblasLtMatmulAlgoGetHeuristic(), then reuse the result for subsequent computations using hipblasLtMatmul().