Supercomputers are becoming more heterogeneous. They are composed by several machines with different computation capabilities and different kinds and families of accelerators, such as GPUs or Intel Xeon Phi coprocessors. Programming these machines is a hard task, that requires a deep study of the architectural details, for exploiting efficiently each computational unit. In this paper, we present an extension of a heterogeneous programming model, in order to also support Intel Xeon Phi coprocessors. This contribution extends an existing heterogeneous programming library, by taking advantage of both the GPU communication model and the CPU execution model of the original library. Our experimental results show that using our approach, the programming effort needed for changing the target devices is highly reduced, by for example reusing the 97% of the code between a GPU implementation and a Xeon Phi implementation for the Mandelbrot benchmark.

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Peachy Parallel Assignments are a resource for instructors teaching parallel and distributed programming. These are high-quality assignments, previously tested in class, that are readily adoptable. This collection of assignments includes implementing a subset of OpenMP using pthreads, creating an animated fractal, image processing using histogram equalization, simulating a storm of high-energy particles, and solving the wave equation in a variety of settings. All of these come with sample assignment sheets and the necessary starter code.

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Abstract. Multi-agent systems allow the modelling of complex, heterogeneous, and distributed systems in a realistic way. MARL-Ped is a multi-agent system tool, based on the MPI standard, for the simulation of different scenarios of pedestrians who autonomously learn the best behavior by Reinforcement Learning. MARL-Ped uses one MPI process for each agent by design, with a fixed fine-grain granularity. This requirement limits the performance of the simulations for a restricted number of processors that is lesser than the number of agents. On the other hand, Hitmap is a library to ease the programming of parallel applications based on distributed arrays. It includes abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as functionalities to build flexible communication patterns that transparently adapt to the data partitions. In this work, we present the methodology and techniques of granularity selection in Hitmap, applied to the simulations of agent systems. As a first approximation, we use the MARL-Ped multi-agent pedestrian simulation software as a case of study for intra-node cases. Hitmap allows to transparently map agents to processes, reducing oversubscription and intra-node communication overheads. The evaluation results show significant advantages when using Hitmap, increasing the flexibility, performance, and agent-number scalability for a fixed number of processing elements, allowing a better exploitation of isolated nodes.