With the increased popularity of multi-GPU nodes in modern HPC clusters, it is imperative to develop matching programming paradigms for their efficient utilization. In order to take advantage of the local GPUs and the low-latency high-throughput interconnects that link them, programmers need to meticulously adapt parallel applications with respect to load balancing, boundary conditions and device synchronization. This paper presents MAPS-Multi, an automatic multi-GPU partitioning framework that distributes the workload based on the underlying memory access patterns. The framework consists of host- and device-level APIs that allow programs to efficiently run on a variety of GPU and multi-GPU architectures. The framework implements several layers of code optimization, device abstraction, and automatic inference of inter-GPU memory exchanges. The paper demonstrates that the performance of MAPS-Multi achieves near-linear scaling on fundamental computational operations, as well as real-world applications in deep learning and multivariate analysis.

Summary Management of forthcoming exascale clusters requires frequent collection of run‐time information about the nodes and the running applications. This paper presents a new paradigm for providing online information to the management system of scalable clusters, consisting of a large number of nodes and one or more masters that manage these nodes. We describe the details of resilient gossip algorithms for sharing local information within subsets of nodes and for sending global information to a master, which holds information on all the nodes. The presented algorithms are decentralized, scalable and resilient, working well even when some nodes fail, without needing any recovery protocol. The paper gives formal expressions for approximating the average ages of the local information at each node and the information collected by the master. It then shows that these results closely match the results of simulations and measurements on a real cluster. The paper also investigates the resilience of the algorithms and the impact on the average age when nodes or masters fail. The main outcome of this paper is that partitioning of large clusters can improve the quality of information available to the management system without increasing the number of messages per node. Copyright © 2015 John Wiley & Sons, Ltd.

GPUs play an increasingly important role in high-performance computing. While developing naive code is straightforward, optimizing massively parallel applications requires deep understanding of the underlying architecture. The developer must struggle with complex index calculations and manual memory transfers. This article classifies memory access patterns used in most parallel algorithms, based on Berkeley’s Parallel “Dwarfs.” It then proposes the MAPS framework, a device-level memory abstraction that facilitates memory access on GPUs, alleviating complex indexing using on-device containers and iterators. This article presents an implementation of MAPS and shows that its performance is comparable to carefully optimized implementations of real-world applications.

Proportional (fair) share schedulers are designed to provide applications with predefined portions of system resources. Single node operating systems use context-switch (preemption) to dynamically allocate the CPU(s) to running processes. This paper presents an online algorithm for proportional share allocations of nodes in a cluster, in a fashion that resembles a single-node system. The algorithm relies on preemptive process migrations for dynamic allocations of nodes to users. The paper presents the algorithm and its performance on a MOSIX organizational Grid with 60 nodes. We show that proportional share allocations can be achieved in a relatively short time (minutes).