The increase in complexity, diversity and scale of high performance computing environments, as well as the increasing sophistication of parallel applications and algorithms call for productivity-aware programming languages for high-performance computing. Among them, the Chapel programming language stands out as one of the more successful approaches based on the Partitioned Global Address Space programming model. Although Chapel is designed for productive parallel computing at scale, the question of its competitiveness with well-established conventional parallel programming environments arises. To this end, this work compares the performance of Chapel-based fractal generation on shared-and distributed-memory platforms with corresponding OpenMP and MPI+X implementations. The parallel computation of the Mandelbrot set is chosen as a test-case for its high degree of parallelism and its irregular workload. Experiments are performed on a cluster composed of 192 cores using the French national testbed Grid'5000. Chapel as well as its default tasking layer demonstrate high performance in shared-memory context, while Chapel competes with hybrid MPI+OpenMP in distributed-memory environment.
Solute mass transfer from a spherical fluid-filled rigid capsule subjected to shear flow is studied numerically, while considering unsteady, continuous, and nonuniform boundary conditions on its surface. Here, the capsule acts as a reservoir with its initially encapsulated solute concentration decaying over time. This scenario differs from the classical case study of either constant concentration or constant mass flux at the surface of the particle. The flow and the concentration field are computed using fully three-dimensional lattice Boltzmann simulations, where the fluid-structure two-way coupling is achieved by the immersed boundary method. The effects of the flow and the boundary conditions on mass transfer efficacy are quantified by the Sherwood number (the dimensionless mass transfer coefficient), which is found to increase due to the combined effects of forced convection and the rotation of the capsule. Having continuity of both the concentration and the mass flux on the capsule significantly decreases the Sherwood number as compared to the case with constant and uniform boundary condition. All the obtained results can be applied to heat transfer in the case of cooling an initially hot spherical particle, for which the concentration must be replaced by the temperature and the Sherwood number by the Nusselt number.
With the recent arrival of the exascale era, modern supercomputers are increasingly big making their programming much more complex. In addition to performance, software productivity is a major concern to choose a programming language, such as Chapel, designed for exascale computing. In this paper, we investigate the design of a parallel distributed tree‐search algorithm, namely P3D‐DFS, and its implementation using Chapel. The design is based on the Chapel's DistBag data structure, revisited by: (1) redefining the data structure for Depth‐First tree‐Search (DFS), henceforth renamed DistBag‐DFS; (2) redesigning the underlying load balancing mechanism. In addition, we propose two instantiations of P3D‐DFS considering the Branch‐and‐Bound (B&B) and Unbalanced Tree Search (UTS) algorithms. In order to evaluate how much performance is traded for productivity, we compare the Chapel‐based implementations of B&B and UTS to their best‐known counterparts based on traditional OpenMP (intra‐node) and MPI+X (inter‐node). For experimental validation using 4096 processing cores, we consider the permutation flow‐shop scheduling problem for B&B and synthetic literature benchmarks for UTS. The reported results show that P3D‐DFS competes with its OpenMP baselines for coarser‐grained shared‐memory scenarios, and with its MPI+X counterparts for distributed‐memory settings, considering both performance and productivity‐awareness. In the context of this work, this makes Chapel an alternative to OpenMP/MPI+X for exascale programming.
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