With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for almost two decades [1]. This paper presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

The main objective of the ADMIRE project1 is the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, the elasticity of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy, while offering quality-of-service (QoS), energy efficiency, and resilience for accessing extremely large data sets in very heterogeneous computing and storage environments. We have developed a framework prototype that is able to dynamically adjust computation and storage requirements through intelligent global coordination, separated control, and data paths, the malleability of computation and I/O, the scheduling of storage resources along all levels of the storage hierarchy, and scalable monitoring techniques. The leading idea in ADMIRE is to co-design applications with ad-hoc storage systems that can be deployed with the application and adapt their computing and I/O behaviour on runtime, using malleability techniques, to increase the performance of applications and the throughput of the applications.

High-Performance Computing (HPC) is currently facing significant challenges. The hardware pressure has become increasingly difficult to manage due to the lack of parallel abstractions in applications. As a result, parallel programs must undergo drastic evolution to effectively exploit underlying hardware parallelism. Failure to do so results in inefficient code. In this constrained environment, parallel runtimes play a critical role, and their testing becomes crucial. This paper focuses on the MPI interface and leverages the MPI binding tools to develop a multi-language test suite for MPI. By doing so and building on previous work from the Forum document editors, we implement a systematic testing of MPI symbols in the context of the Parallel Computing Validation System (PCVS), which is an HPC validation platform dedicated to running and managing test suites at scale. We first describe PCVS, then outline the process of generating the MPI API test suite, and finally, run these tests at scale. All data sets, code generators, and implementations are made available in open-source to the community. We also set up a dedicated website showcasing the results, which self-updates thanks to the Spack package manager.

MPI is the most widely used interface for high-performance computing (HPC) workloads. Its success lies in its embrace of libraries and ability to evolve while maintaining backward compatibility for older codes, enabling them to run on new architectures for many years. In this paper, we propose a new level of MPI compatibility: a standard Application Binary Interface (ABI). We review the history of MPI implementation ABIs, identify the constraints from the MPI standard and ISO C, and summarize recent efforts to develop a standard ABI for MPI. We provide the current proposal from the MPI Forum’s ABI working group, which has been prototyped both within MPICH and as an independent abstraction layer called Mukautuva. We also list several use cases that would benefit from the definition of an ABI while outlining the remaining constraints.

Distributed software using MPI is now facing a complexity barrier. Indeed, given increasing intra-node parallelism, combined with the use of accelerator, programs’ states are becoming more intricate. A given code must cover several cases, generating work for multiple devices. Model mixing generally leads to increasingly large programs and hinders performance portability. In this paper, we pose the question of software composition, trying to split jobs in multiple services. In doing so, we advocate it would be possible to depend on more suitable units while removing the need for extensive runtime stacking (MPI+X+Y). For this purpose, we discuss what MPI shall provide and what is currently available to enable such software composition. After pinpointing (1) process discovery and (2) Remote Procedure Calls (RPCs) as facilitators in such infrastructure, we focus solely on the first aspect. We introduce an overlay-network providing whole-machine inter-job, discovery, and wiring at the level of the MPI runtime. MPI process Unique IDentifiers (UIDs) are then covered as a Unique Resource Locator (URL) leveraged as support for job interaction in MPI, enabling a more horizontal usage of the MPI interface. Eventually, we present performance results for large-scale wiring-up exchanges, demonstrating gains over PMIx in cross-job configurations.

Since the beginning, MPI has defined the rank as an implicit attribute associated with the MPI process' environment. In particular, each MPI process generally runs inside a given UNIX process and is associated with a fixed identifier in its WORLD communicator. However, this state of things is about to change with the rise of new abstractions such as MPI Sessions. In this paper, we propose to outline how such evolution could enable optimizations which were previously linked to specific MPI runtimes executing MPI processes in shared memory (e.g. thread-based MPI). By implementing runtime-level work-sharing through what we define as MPI tasks, enabling the ability to progress indifferently from stream context we show that there is potential for improved asynchronous progress. In the absence of a Session implementation, this assumption is validated in the context of a thread-based MPI where nonblocking Collective (NBC) were implemented on top of Extended Generic Requests progressed by any rank on the node thanks to an MPI extension enabling threads to dynamically share their MPI context.

Fault-tolerance has always been an important topic when it comes to running massively parallel programs at scale. Statistically, hardware and software failures are expected to occur more often on systems gathering millions of computing units. Moreover, the larger jobs are, the more computing hours would be wasted by a crash. In this paper, we describe the work done in our MPI runtime to enable transparent checkpointing mechanism. Unlike the MPI 4.0 User-Level Failure Mitigation (ULFM) interface, our work targets solely Checkpoint/Restart (C/R) and ignores wider features such as resiliency. We show how existing transparent checkpointing methods can be practically applied to MPI implementations given a sufficient collaboration from the MPI runtime. Our C/R technique is then measured on MPI benchmarks such as IMB and Lulesh relying on Infiniband high-speed network, demonstrating that the chosen approach is sufficiently general and that performance is mostly preserved. We argue that enabling fault-tolerance without any modification inside target MPI applications is possible, and show how it could be the first step for more integrated resiliency combined with failure mitigation like ULFM.

The advent of many-core architectures poses new challenges to the MPI programming model which has been designed for distributed memory message passing. It is now clear that MPI will have to evolve in order to exploit shared-memory parallelism, either by collaborating with other programming models (MPI+X) or by introducing new shared-memory approaches. This paper considers extensions to C and C++ to make it possible for MPI Processes to run into threads. More generally, a thread-local storage (TLS) library is developed to simplify the collocation of arbitrary tasks and services in a shared-memory context called a task-container. The paper discusses how such containers simplify model and service mixing at the OS process level, eventually easing the collocation of arbitrary tasks with MPI processes in a runtime agnostic fashion, opening alternatives to runtime stacking.

In the race for Exascale, the advent of many-core processors will bring a shift in parallel computing architectures to systems of much higher concurrency, but with a relatively smaller memory per thread. This shift raises concerns for the adaptability of HPC software, for the current generation to the brave new world. In this paper, we study domain splitting on an increasing number of memory areas as an example problem where negative performance impact on computation could arise. We identify the specific parameters that drive scalability for this problem, and then model the halo-cell ratio on common mesh topologies to study the memory and communication implications. Such analysis argues for the use of shared-memory parallelism, such as with OpenMP, to address the performance problems that could occur. In contrast, we propose an original solution based entirely on MPI programming semantics, while providing the performance advantages of hybrid parallel programming. Our solution transparently replaces halo-cells transfers with pointer exchanges when MPI tasks are running on the same node, effectively removing memory copies. The results we present demonstrate gains in terms of memory and computation time on Xeon Phi (compared to OpenMP-only and MPI-only) using a representative domain decomposition benchmark.

As the power of supercomputers is exponentially increasing, programmers are facing complex codes designed to comply with today's challenging architectural constraints. In such context, the use of tools within the development cycle, is becoming crucial in order to optimise applications at scale. However, it is not possible to obtain all measurements one can think of, because of the cost to produce, store and analyse large amounts of instrumentation-data. Moreover, the file-system is becoming a critical resource, subject to performance and even stability problems under load. This emphasises the need for an alternative approach to trace data management. This paper proposes an alternative to trace-based coupling between instrumentation and analysis. We present a distributed analysis engine, providing concurrent application profiling, thanks to runtime coupling. After demonstrating the advantages of this method in terms of parallelism, we present performance results and sample outputs for NAS-MPI benchmarks and a representative C++ MPI application.