The International Journal of High Performance Computing Applications

MPI defines a one-to-one relationship between MPI processes and ranks. This model captures many use cases effectively; however, it also limits communication concurrency and interoperability between MPI and programming models that utilize threads. This paper describes the MPI endpoints extension, which relaxes the longstanding one-to-one relationship between MPI processes and ranks. Using endpoints, an MPI implementation can map separate communication contexts to threads, allowing them to drive communication independently. Endpoints also enable threads to be addressable in MPI operations, enhancing interoperability between MPI and other programming models. These characteristics are illustrated through several examples and an empirical study that contrasts current multithreaded communication performance with the need for high degrees of communication concurrency to achieve peak communication performance.

We present DRASync, a region-based allocator that implements a global address space abstraction for MPI programs with pointer-based data structures. The main features of DRASync are: (a) it amortizes communication among nodes to allow efficient parallel allocation in a global address space; (b) it takes advantage of bulk deallocation and good locality with pointer-based data structures; (c) it supports ownership semantics of regions by nodes akin to reader–writer locks, which makes for a high-level, intuitive synchronization tool in MPI programs, without sacrificing message-passing performance. We evaluate DRASync against a state-of-the-art distributed allocator and find that it produces comparable performance while offering a higher-level abstraction to programmers.

Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that may combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way in which compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.

Nowadays most scientific applications are parallelized based on MPI communications. Collective MPI communications have to be executed in the same order by all processes in their communicator and the same number of times, otherwise they do not conform to the standard and a deadlock or other undefined behaviour can occur. As soon as the control flow involving these collective operations becomes more complex, in particular including conditionals on process ranks, ensuring the correction of such code is error-prone. We propose in this paper a static analysis to detect when such a situation occurs, combined with a code transformation that prevents deadlocking. We focus on blocking MPI collective operations in single program multiple data applications, assuming MPI calls are not nested in multithreaded regions. We show on several benchmarks the small impact on performance and the ease of integration of our techniques in the development process.

The message passing interface (MPI) is one of the most portable high-performance computing (HPC) programming models, with platform-optimized implementations typically delivered with new HPC systems. Therefore, for distributed services requiring portable, high-performance, user-level network access, MPI promises to be an attractive alternative to custom network portability layers, platform-specific methods, or portable but less performant interfaces such as BSD sockets. In this paper, we present our experiences in using MPI as a network transport for a large-scale distributed storage system. We discuss the features of MPI that facilitate adoption as well as aspects which require various workarounds. Based on use cases, we derive a wish list for both MPI implementations and the MPI forum to facilitate the adoption of MPI by large-scale persistent services. The proposals in the wish list go beyond the sole needs of distributed services; we contend that they will benefit mainstream HPC applications at extreme scales as well.

The Blue Gene/Q (BG/Q) machine is the latest in the line of IBM massively parallel supercomputers, designed to scale to 262,144 nodes and 16 million threads. Each BG/Q node has 68 hardware threads. Hybrid programming paradigms, which use message passing among nodes and multi-threading within nodes, enable applications to achieve high throughput on BG/Q. In this paper, we present scalable algorithms to optimize MPI collective operations by taking advantage of the various features of the BG/Q torus and collective networks. We achieve an 8 byte double-sum MPI_Allreduce latency of 10.25 ms on 1,572,864 MPI ranks. We accelerate summing of network packets with local buffers by the use of the Quad Processing SIMD unit in the BG/Q cores and executing the sums on multiple communication threads supported by the optimized communication libraries. The achieved net gain is a peak throughput of 6.3 GB/s for double-sum allreduce. We also achieve over 90% of network peak for MPI_Alltoall with 65,536 MPI ranks.