Random program generation — fuzzing — is an effective technique for discovering bugs in compilers but successful fuzzers require extensive development effort for every language supported by the compiler, and often leave parts of the language space untested.

Heterogeneous-ISA computing platforms have become ubiquitous, and will be used for diverse workloads which render static mappings of computation to processors inadequate. Dynamic mappings which adjust an application's usage in consideration of platform workload can reduce application latency and increase throughput for heterogeneous platforms. We introduce AIRA, a compiler and runtime for flexible execution of applications in CPU-GPU platforms. Using AIRA, we demonstrate up to a 3.78× speedup in benchmarks from Rodinia and Parboil, run with various workloads on a server-class platform. Additionally, AIRA is able to extract up to an 87 percent increase in platform throughput over a static mapping.

The recent possibility of integrating multiple-OS-capable, high-core-count, heterogeneous-ISA processors in the same platform poses a question: given the tight integration between system components, can a shared memory programming model be adopted, enhancing programmability? If this can be done, an enormous amount of existing code written for shared memory architectures would not have to be rewritten to use a new programming paradigm (e.g., code offloading) that is often very expensive and error prone. We propose a new software architecture that is composed of an operating system and a compiler framework to run ordinary shared memory applications, written for homogeneous machines, on OS-capable heterogeneous-ISA machines. Applications run transparently amongst different ISA processors while exploiting the most optimized instruction set for each code block. We have implemented and tested our system, called Popcorn, on a multi-core Intel Xeon machine with a PCIe Intel Xeon Phi to demonstrate the viability of our approach. Application execution on Popcorn demonstrates to be up to 52% faster than the most performant native execution on Linux, on either Xeon or Xeon Phi, while removing the burden of the programmer having to adopt a different programming model than shared memory on a heterogeneous system. When compared to an offloading programming model, Popcorn is shown to be up to 6.2 times faster.