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Task-based programming models are a promising approach to exploiting complex distributed and heterogeneous systems. However, integrating different communication, offloading, and storage APIs within tasks poses performance and deadlock risks. Several Task-Aware libraries, such as TAMPI, TASIO, and TACUDA, have been developed to integrate blocking and non-blocking APIs within task-based programming models efficiently. In this paper, we introduce the Asynchronous Low-level Programming Interface (ALPI) to enable the interoperability and portability of Task-Aware libraries across various programming models and runtime systems. We have implemented ALPI in the Nanos6 and nOS-V runtimes, enhancing the integration of Task-Aware libraries with the OmpSs-2 and OpenMP programming models. This work is a step towards improving the composability of parallel programming models by supporting Task-Aware libraries across different runtime systems., This work was supported by the Spanish Ministry of Science and Innovation (grant PID2019-107255GB) and the Severo Ochoa Program (grant CEX2021-001148-S), both funded by MCIN/AEI/10.13039/501100011033. The Generalitat de Catalunya also supported this work via grant 2021-SGR-01007., Peer Reviewed, Postprint (author's final draft)

Processors with multiple sockets or chiplets are becoming more conventional. These kinds of processors usually expose a single shared address space. However, due to hardware restrictions, they adopt a NUMA approach, where each processor accesses local memory faster than remote memories. Reducing data motion is crucial to improve the overall performance. Thus, computations must run as close as possible to where the data resides. We propose a new approach that mitigates the NUMA effect on NUMA systems. Our solution is based on the OmpSs-2 programming model, a task-based parallel programming model, similar to OpenMP. We first provide a simple API to allocate memory in NUMA systems using different policies. Then, combining user-given information that specifies dependences between tasks, and information collected in a global directory when allocating data, we extend our runtime library to perform NUMA-aware work scheduling. Our heuristic considers data location, distance between NUMA nodes, and the load of each NUMA node to seamlessly minimize data motion costs and load imbalance. Our evaluation shows that our NUMA support can significantly mitigate the NUMA effect by reducing the amount of remote accesses, and so improving performance on most benchmarks, reaching up to 2x speedup in a 2-NUMA machine, and up to 7.1x in a 8-NUMA machine., This research has received funding from the European Union’s Horizon 2020/EuroHPC research and innovation programme under grant agreement No 955606 (DEEP-SEA), project PCI2021121958 financed by the Spanish State Research Agency - Ministry of Science and Innovation, Generalitat de Catalunya (contract 2021-SGR-01007), the Spanish Ministry of Science and Technology (contract PID2019-107255GB), and Severo Ochoa (CEX2021- 001148-S / MCIN/AEI /10.13039/501100011033)., Peer Reviewed, Postprint (author's final draft)

The general matrix-matrix multiplication (GEMM) kernel is a fundamental building block of many scientific applications. Many libraries such as Intel MKL and BLIS provide highly optimized sequential and parallel versions of this kernel. The parallel implementations of the GEMM kernel rely on the well-known fork-join execution model to exploit multi-core systems efficiently. However, these implementations are not well suited for task-based applications as they break the data-flow execution model. In this paper, we present a task-based implementation of the GEMM kernel that can be seamlessly leveraged by task-based applications while providing better performance than the fork-join version. Our implementation leverages several advanced features of the OmpSs-2 programming model and a new heuristic to select the best parallelization strategy and blocking parameters based on the matrix and hardware characteristics. When evaluating the performance and energy consumption on two modern multi-core systems, we show that our implementations provide significant performance improvements over an optimized OpenMP fork-join implementation, and can beat vendor implementations of the GEMM (e.g., Intel MKL and AMD AOCL). We also demonstrate that a real application can leverage our optimized task-based implementation to enhance performance., Peer Reviewed, Postprint (author's final draft)