As Moore's law comes to an end, chipmakers are increasingly relying on both heterogeneous and parallel architectures for performance gains. This has led to a diverse set of software tools and paradigms such as CUDA, OpenMP, Cilk, and many others to best exploit a program’s parallelism for performance gain. Yet, while such tools provide us ways to express parallelism, they come at a large cost to the programmer, requiring in depth knowledge of what to parallelize, how to best map the parallelism to the hardware, and having to rework the code to match the programming model chosen by the software tool.
In this talk, we discuss how to use Tapir, a parallel extension to LLVM, to optimize parallel programs. We will show how one can use Tapir/LLVM to represent programs in attendees’ favorite parallel framework by extending clang, how to perform various optimizations on parallel code, and how to to connect attendees’ parallel language to a variety of parallel backends for execution (PTX, OpenMP Runtime, Cilk Runtime).
