Abstract Lightweight manycore processors deliver high performance and energy efficiency by bundling hundreds of low‐power cores, a distributed memory architecture with small local memories and Networks‐on‐Chip in a single die. However, the lack of rich and portable programming models for these processors makes software development a challenging task. Currently, two approaches are employed to address programmability in lightweight manycores: Operating Systems (OSes) and baremetal runtime libraries. The former provides portability but exposes complex Operating System (OS)‐level programming interfaces to developers. The latter focuses on providing rich and high performance interfaces, which are vendor‐specific and yield to non‐portable software. In this work, we address these programmability and portability challenges by combining a rich OS with a well‐known standard for parallel programming. We propose a portable and lightweight Message Passing Interface (MPI) library (LWMPI) designed from scratch to cope with restrictions and intricacies of lightweight manycores. We integrated LWMPI into Nanvix, an open‐source distributed OS that runs on silicon lightweight manycores. The results obtained with a synthetic benchmark and a subset of the CAP Bench applications running on Kalray MPPA‐256 unveil that LWMPI not only delivers a lightweight and richer programming interface but also presents good performance and scalability results.

Operating Systems (OSes) for lightweight manycore processors feature a distributed design, where isolated OS instances cooperate to improve programmability and portability issues that come from their architectural intricacies. However, OS services often resort to processes or threads to implement kernel-level functionalities, consuming much of the already limited on-chip memory available in these processors. In this context, we propose a complementary OS-level execution engine that supports cooperative time-sharing lightweight tasks that share a unique execution stack and features task synchronization via task dependency graphs. This solution provides numerous OS-level execution flows with reduced memory consumption, leaving more room for user-level applications. We implemented our solution in an open-source distributed OS (Nanvix) and we compared it with the standard implementation that uses threads. The results obtained on Kalray MPPA-256 show that our engine: (i) provides 63.2x more execution flows per MB of memory; (ii) features 15.8x and 1.87x less overhead to spawn and destroy an execution flow, respectively, while consuming 6.65x less energy; (iii) runs remote system calls 3x faster; and (iv) reduces the memory footprint in 0.58x when executing OS services requests without impacting the overall system performance.