As future computing hardware progresses towards extreme-scale technology, new challenges arise for addressing heterogeneous compute and memory resources, for providing application resilience in the presence of more frequent failures, and for working within strict energy constraints. While C++ has gained popularity in recent years within the HPC community, some concepts of object-oriented program design may be at odds with the techniques we use to address the challenges of extreme-scale computing. In this work, we focus on the challenges related to using aggregate data structures that include pointer values within a programming model where the runtime may frequently relocate data, and traditional serialization techniques are not practical. We propose and evaluate a marshalled encoding for relocatable data blocks, and present a C++ library and other tools to simplify the work of the application programmer developing new applications or porting existing applications to such emerging programming models.

Optimized placement of data and computation for locality is critical for improving performance and reducing energy consumption on modern computing systems. However, for most programming models, modifying data and computation placements typically requires rewriting large portions of the application, thereby posing a huge performance portability challenge in today's rapidly evolving architecture landscape. In this paper we present TunedCnC, a novel, declarative and flexible CnC tuning framework for controlling the spatial and temporal placement of data and computation by specifying hierarchical affinity groups and distribution functions. TunedCnC emphasizes a separation of concerns: the domain expert specifies a parallel application by defining data and control dependences, while the tuning expert specifies how the application should be executed on a given architecture - defining when and where for data and computation placement. The application remains unchanged when tuned for a different platform or towards different performance goals. We evaluate the utility of TunedCnC on several applications, and demonstrate that varying the tuning specification can have a significant impact on an application's performance. Our evaluation is performed using an implementation of the Concurrent Collections (CnC) declarative parallel programming model, but our results should be applicable to tuning of other data-flow task-parallel programming models as well.

Habanero is a task parallel programming model that provides correctness guarantees to the programmer. Even so, programs may contain data races that lead to non-determinism, which complicates debugging and verification. This paper presents a sound algorithm based on permission regions to prove data race and deadlock freedom in Habanero programs. Permission regions are user annotations to indicate the use of shared variables over spans of code. The verification algorithm restricts scheduling to permission region boundaries and isolation to reduce verification cost. The effectiveness of the algorithm is shown in benchmarks with an implementation in the Java Pathfinder (JPF) model checker. The implementation uses a verification specific library for Habanero that is tested using JPF for correctness. The results show significant reductions in cost, where cost is controlled with the size of the permission regions, at the risk of rejecting programs that are actually free of any data race or deadlock.

Coordination and synchronization of parallel tasks is a major source of complexity in parallel programming. These constructs take many forms in practice including directed barrier and point-to-point synchronizations, termination detection of child tasks, and mutual exclusion in accesses to shared resources.