A programming model for deterministic task parallelism

The currently dominant programming models to write software for multicore processors use threads that run over shared memory. However, as the core count increases, cache coherency protocols get very complex and ineffective, and maintaining a shared memory abstraction becomes expensive and impractical. Moreover, writing multithreaded programs is notoriously difficult, as the programmer needs to reason about all the possible thread interleavings and in- teractions, including the myriad of implicit, non-obvious, and often unpredictable thread interactions through shared memory. Overall, as processors get more cores and parallel software becomes main- stream, the shared memory model reaches its limits regarding ease of programming and efficiency. This position paper presents two ideas aiming to solve the prob- lem. First, we restrict the way the programmer expresses paral- lelism: The program is a collection of possibly recursive tasks, where each task is atomic and cannot communicate with any other task during its execution. Second, we relax the requirement for co- herent shared memory: Each task defines its memory footprint, and is guaranteed to have exclusive access to that memory during its execution. Using this model, we can then define a runtime system that transparently performs the data transfers required among cores without cache coherency, and also produces a deterministic execu- tion of the program, provably equivalent to its sequential elision