Memory models: a case for rethinking parallel languages and hardware

The era of parallel computing for the masses is here, but writing correct parallel programs remains a challenge--popular parallel environments offer no analogs for the concepts of structured and safe sequential programming. The memory model forms the heart of the concurrency semantics of any (shared-memory) parallel language or hardware. Unfortunately, it has involved a tradeoff between programmability and performance, and has arguably been one of the most challenging and contentious areas in shared-memory specification. Recent broad community-scale efforts have finally led to a convergence in this debate, with popular languages such as Java and C++ and most hardware vendors publishing compatible memory model specifications. Although this convergence is a dramatic improvement, it has exposed fundamental shortcomings in current popular languages and systems that prevent achieving the vision of structured and safe parallel programming. I will discuss the path to the above convergence, the hard lessons learned, and their implications. A cornerstone of this convergence has been the view that the memory model should be a contract between the programmer and the system--if the programmer writes well-structured (data-race-free) programs, the system will provide high programmability (sequential consistency) and performance. I will discuss why this view is the best we can do with current popular languages, and why it should be unacceptable moving forward. I will then discuss research directions that eliminate worrying about the memory model, but require rethinking popular parallel languages and hardware. In particular, I will argue for languages that eliminate data races by design and provide determinism by default, while retaining the advantages of modern object-oriented programming. I will also argue for hardware that takes advantage of such disciplined programming models to enable energy-efficient performance scalability. I will use the Deterministic Parallel Java language and DeNovo hardware projects at Illinois as examples of such directions.