Task-parallel languages are increasingly popular. Many of them provide expressive mechanisms for intertask synchronization. For example, OpenMP 4.0 will integrate data-driven execution semantics derived from the StarSs research language. Compared to the more restrictive data-parallel and fork-join concurrency models, the advanced features being introduced into task-parallel models in turn enable improved scalability through load balancing, memory latency hiding, mitigation of the pressure on memory bandwidth, and, as a side effect, reduced power consumption.In this article, we develop a systematic approach to compile loop nests into concurrent, dynamically constructed graphs of dependent tasks. We propose a simple and effective heuristic that selects the most profitable parallelization idiom for every dependence type and communication pattern. This heuristic enables the extraction of interband parallelism (cross-barrier parallelism) in a number of numerical computations that range from linear algebra to structured grids and image processing. The proposed static analysis and code generation alleviates the burden of a full-blown dependence resolver to track the readiness of tasks at runtime. We evaluate our approach and algorithms in the PPCG compiler, targeting OpenStream, a representative dataflow task-parallel language with explicit intertask dependences and a lightweight runtime. Experimental results demonstrate the effectiveness of the approach.

We present a joint scheduling and memory allocation algorithm for efficient execution of task-parallel programs on non-uniform memory architecture (NUMA) systems. Task and data placement decisions are based on a static description of the memory hierarchy and on runtime information about intertask communication. Existing locality-aware scheduling strategies for fine-grained tasks have strong limitations: they are specific to some class of machines or applications, they do not handle task dependences, they require manual program annotations, or they rely on fragile profiling schemes. By contrast, our solution makes no assumption on the structure of programs or on the layout of data in memory. Experimental results, based on the Open-Stream language, show that locality of accesses to main memory of scientific applications can be increased significantly on a 64-core machine, resulting in a speedup of up to 1.63× compared to a state-of-the-art work-stealing scheduler.

ISBN: 978-145030241-8International audienceThis paper introduces an extension to OpenMP3.0 enabling stream programming with minimal, incremental additions that seamlessly integrate into the current specification. The stream programming model decomposes programs into tasks and explicits the flow of data among them, thus exposing data, task and pipeline parallelism. It helps the programmers to express concurrency and data locality properties, avoiding non-portable low-level code and early optimizations. We survey the diverse motivations and constraints converging towards the design of our simple yet powerful language extension, and we present experimental results of a prototype implementation in a public branch of GCC 4.5

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Chase and Lev's concurrent deque is a key data structure in sharedmemory parallel programming and plays an essential role in workstealing schedulers. We provide the first correctness proof of an optimized implementation of Chase and Lev's deque on top of the POWER and ARM architectures: these provide very relaxed memory models, which we exploit to improve performance but considerably complicate the reasoning. We also study an optimized x86 and a portable C11 implementation, conducting systematic experiments to evaluate the impact of memory barrier optimizations. Our results demonstrate the benefits of hand tuning the deque code when running on top of relaxed memory models.