THAPI (Tracing Heterogeneous APIs) is an open-source tracing infrastructure for HPC platforms that use accelerators, developed at Argonne National Laboratory. It intercepts the low-level API calls (L0, CUDA driven, Cuda Runtime, HIP, OpenCL, OpenMP) in order to dump their arguments and timestamps in CTF format using LTTng. The traces can be analyzed postmortem leveraging babeltrace2 and plugin infrastructure. We’ve developed plugins to fulfill our multiple use cases (tally, timeline, pretty printer, validation).

We routinely generate traces with billions of events produced by hundreds of threads. Our instrumentation is achieved by preloading libraries without recompiling the target applications. THAPI and the associated plugins have been explicitly developed for HPC applications. They can handle multi-process, multi-threaded, multi-device applications.

We will share hands-on experiences starting with how we designed our modular approach to tracing to be future-proof and how it enabled us to incorporate new APIs. We heavily leverage meta-programming to generate LTTng tracepoints and Babeltrace plugins.

Application developers and performance engineers use THAPI to provide lightweight profiling of HPC applications, validate correct usages of traced APIs, and diagnose functional or performance bugs in higher-level runtimes. THAPI has proven to be an invaluable and popular tool for our HPC community.

Biography - Thomas Applencourt

Thomas Applencourt is a Computational Scientist at Argonne National Laboratory interested in HPC. He is now working on the Aurora exascale Computing system. He is interested in various programming models (OpenMP, SYCL) and low-level programming.

Biography - Brice Videau

Brice Videau is a Computer Scientist working at Argonne National Laboratory. His main interests are Heterogeneous programming models, system programming, meta-programming, and auto-tuning.