In this paper we present the Task-Aware MPI library (TAMPI) that integrates both blocking and non-blocking MPI primitives with task-based programming models. The TAMPI library leverages two new runtime APIs to improve both programmability and performance of hybrid applications. The first API allows to pause and resume the execution of a task depending on external events. This API is used to improve the interoperability between blocking MPI communication primitives and tasks. When an MPI operation executed inside a task blocks, the task running is paused so that the runtime system can schedule a new task on the core that became idle. Once the blocked MPI operation is completed, the paused task is put again on the runtime system's ready queue, so eventually it will be scheduled again and its execution will be resumed.The second API defers the release of dependencies associated with a task completion until some external events are fulfilled. This API is composed only of two functions, one to bind external events to a running task and another function to notify about the completion of external events previously bound. TAMPI leverages this API to bind non-blocking MPI operations with tasks, deferring the release of their task dependencies until both task execution and all its bound MPI operations are completed.Our experiments reveal that the enhanced features of TAMPI not only simplify the development of hybrid MPI+OpenMP applications that use blocking or non-blocking MPI primitives but they also naturally overlap computation and communication phases, which improves application performance and scalability by removing artificial dependencies across communication tasks.

In this paper we propose an API to pause and resume task execution depending on external events. We leverage this generic API to improve the interoperability between MPI synchronous communication primitives and tasks. When an MPI operation blocks, the task running is paused so that the runtime system can schedule a new task on the core that became idle. Once the MPI operation is completed, the paused task is put again on the runtime system's ready queue. We expose our proposal through a new MPI threading level which we implement through two approaches.The first approach is an MPI wrapper library that works with any MPI implementation by intercepting MPI synchronous calls, implementing them on top of their asynchronous counterparts. In this case, the task-based runtime system is also extended to periodically check for pending MPI operations and resume the corresponding tasks once MPI operations complete. The second approach consists in directly modifying the MPICH runtime system, a well-known implementation of MPI, to directly call the pause/resume API when a synchronous MPI operation blocks and completes, respectively.Our experiments reveal that this proposal not only simplifies the development of hybrid MPI+OpenMP applications that naturally overlap computation and communication phases; it also improves application performance and scalability by removing artificial dependencies across communication tasks. Using the comm thread approach of the hybrid MPI+SMPSs programming model [9], authors allowed to exploit distant parallelism separated by taskified MPI calls. These tasks were also identified as communication tasks and were executed by an additional thread called communication thread. The runtime's task scheduler could reorder the execution of communication and computational tasks in such a way that communication can happen as soon as possible, increasing the parallelism within and across MPI processes. That proposal requires changes to the programming model to allow to identify ahead of time those tasks that have blocking-like behavior. In addition, only one thread can execute them, and it must do so in sequential order. Hence, this solution

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks.CCS Concepts: • Computing methodologies → Parallel programming languages.

Shared memory programming models usually provide worksharing and task constructs. The former relies on the efficient fork-join execution model to exploit structured parallelism; while the latter relies on fine-grained synchronization among tasks and a flexible data-flow execution model to exploit dynamic, irregular, and nested parallelism. On applications that show both structured and unstructured parallelism, both worksharing and task constructs can be combined. However, it is difficult to mix both execution models without penalizing the data-flow execution model. Hence, on many applications structured parallelism is also exploited using tasks to leverage the full benefits of a pure data-flow execution model. However, task creation and management might introduce a non-negligible overhead that prevents the efficient exploitation of fine-grained structured parallelism, especially on many-core processors. In this work, we propose worksharing tasks. These are tasks that internally leverage worksharing techniques to exploit fine-grained structured loop-based parallelism. The evaluation shows promising results on several benchmarks and platforms.

Adaptive Mesh Refinement (AMR) is a prevalent method used by distributed-memory simulation applications to adapt the accuracy of their solutions depending on the turbulent conditions in each of their domain regions. These applications are usually dynamic since their domain areas are refined or coarsened in various refinement stages during their execution. Thus, they periodically redistribute their workloads among processes to avoid load imbalance. Although the defacto standard for scientific computing in distributed environments is MPI, in recent years, pure MPI applications are being ported to hybrid ones, attempting to cope with modern multi-core systems. Recently, the Task-Aware MPI library was proposed to efficiently integrate MPI communications and tasking models, providing also the transparent management of communications issued by tasks. In this paper, we demonstrate the benefits of porting AMR applications to data-flow programming models leveraging that novel hybrid approach. We exploit most of the application parallelism by taskifying all stages, allowing their natural overlap. We employ these techniques on the miniAMR proxy application, which mimics the refinement, load balancing, communication, and computation patterns of general AMR applications. We evaluate how this approach reduces the time in its computation and communication phases while achieving better programmability than other conventional hybrid techniques.