MPI applications begin with a fixed number of rank and, by default, the rank remains constant throughout the application's lifetime. The developer can choose to increase the rank by dynamically spawning MPI processes. However doing this manually adds complexity to the MPI application. Making the MPI applications malleable \cite{b20} would allow HPC applications to have the same elasticity as that of cloud applications. We propose multiple approaches to change the rank of an MPI program agnostic to the modification of the user code. We use checkpointing as a tool to achieve mutability of rank by halting the execution and resuming the MPI program with a new state. In this paper, we focus on the scenario of increasing the rank of an MPI program using ExaMPI as the implementation for MPI.
MPI accomplishes portable, standardized message-passing between processes by exposing a standard API that hides the implementation of the underlying mechanism for message passing. Until now, checkpointing an MPI program required knowledge of these underlying mechanisms. Through the addition of a proxy, we demonstrate that MPI programs can be checkpointed and restarted regardless of the MPI implementation utilized. Further, proxies may enable MPI programs to be checkpointed on one MPI implementation, and restarted on another.
Scientific applications often contain large computationally-intensive parallel loops. Loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Mor…
Computationally-intensive loops are the primary source of parallelism in scientific applications. Such loops are often irregular and a balanced execution of their loop iterations is critical for achieving high performance. However, several factors may lead to an imbalanced load execution, such as problem characteristics, algorithmic, and systemic variations. Dynamic loop self-scheduling (DLS) techniques are devised to mitigate these factors, and consequently, improve application performance. On distributed-memory systems, DLS techniques can be implemented using a hierarchical master-worker exe…
Power efficiency has recently become a major concern in the high-performance computing domain. HPC centers are provisioned by a power bound which impacts execution time. Naturally, a tradeoff arises between power efficiency and computational efficiency. This paper tackles the problem of performance optimization for MPI applications, where a power bound is assumed. The paper exposes a subset of HPC applications that leverage cluster parallelism using MPI, where nodes encounter multiple synchronization points and exhibit inter-node dependency. We abstract this structure into a dependency graph, …
Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices. With increasing data volume, distributed memory systems (such as tightly connected supercomputers or cloud computing systems) are becoming important in designing in-memory and massively parallel MLDM algorithms. Yet, the majority of open source MLDM software is limited to sequential execution with a few supporting multi-core/many-core execution. In this paper, we extend recently proposed Google TensorFlow for executio…
MPI has been ubiquitously deployed in flagship HPC systems aiming to accelerate distributed scientific applications running on tens of hundreds of processes and compute nodes. Maintaining the correctness and integrity of MPI application execution is critical, especially for safety-critical scientific applications. Therefore, a collection of effective MPI fault tolerance techniques have been proposed to enable MPI application execution to efficiently resume from system failures. However, there is no structured way to study and compare different MPI fault tolerance designs, so to guide the selec…
The hybrid MPI+X programming paradigm, where X refers to threads or GPUs, has gained prominence in the high-performance computing arena. This corresponds to a trend of system architectures growing more heterogeneous. The current MPI standard only specifies the compatibility levels between MPI and threading runtimes. No MPI concept or interface exists for applications to pass thread context or GPU stream context to MPI implementations explicitly. This lack has made performance optimization complicated in some cases and impossible in other cases. We propose a new concept in MPI, called MPIX stre…
Asynchronous programming models (APM) are gaining more and more traction, allowing applications to expose the available concurrency to a runtime system tasked with coordinating the execution. While MPI has long provided support for multi-threaded communication and non-blocking operations, it falls short of adequately supporting APMs as correctly and efficiently handling MPI communication in different models is still a challenge. Meanwhile, new low-level implementations of light-weight, cooperatively scheduled execution contexts (fibers, aka user-level threads (ULT)) are meant to serve as a bas…
Message Passing Interfaces (MPI) plays an important role in parallel computing. Many parallel applications are implemented as MPI programs. The existing methods of bug detection for MPI programs have the shortage of providing both input and non-determinism coverage, leading to missed bugs. In this paper, we employ symbolic execution to ensure the input coverage, and propose an on-the-fly schedule algorithm to reduce the interleaving explorations for non-determinism coverage, while ensuring the soundness and completeness. We have implemented our approach as a tool, called MPISE, which can autom…
Scaling supercomputers comes with an increase in failure rates due to the increasing number of hardware components. In standard practice, applications are made resilient through checkpointing data and restarting execution after a failure occurs to resume from the latest check-point. However, re-deploying an application incurs overhead by tearing down and re-instating execution, and possibly limiting checkpointing retrieval from slow permanent storage. In this paper we present Reinit++, a new design and implementation of the Reinit approach for global-restart recovery, which avoids application …
We describe a program for the parallel implementation of multiple runs of XSTAR, a photoionization code that is used to predict the physical properties of an ionized gas from its emission and/or absorption lines. The parallelization program, called MPI_XSTAR, has been developed and implemented in the C++ language by using the Message Passing Interface (MPI) protocol, a conventional standard of parallel computing. We have benchmarked parallel multiprocessing executions of XSTAR, using MPI_XSTAR, against a serial execution of XSTAR, in terms of the parallelization speedup and the computing resou…
Lomonosov Moscow State University（莫斯科国立罗蒙诺索夫大学）；Applied Research Center for Computer Networks (ARCCN)（计算机网络应用研究中心）
The problem of MPI programs execution time prediction on a certain set of computer installations is considered. This problem emerges with orchestration and provisioning a virtual infrastructure in a cloud computing environment over a heterogeneous network of computer installations: supercomputers or clusters of servers (e.g. mini data centers). One of the key criteria for the effectiveness of the cloud computing environment is the time staying by the program inside the environment. This time consists of the waiting time in the queue and the execution time on the selected physical computer inst…
Due to the increasing size of HPC machines, the fault presence is becoming an eventuality that applications must face. Natively, MPI provides no support for the execution past the detection of a fault, and this is becoming more and more constraining. With the introduction of ULFM (User Level Fault Mitigation library), it has been provided with a possible way to overtake a fault during the application execution at the cost of code modifications. ULFM is intrusive in the application and requires also a deep understanding of its recovery procedures. In this paper we propose Legio, a framework tha…
Distributed Order Recording Techniques for Efficient Record-and-Replay of Multi-threaded Programs
Nanchang Hangkong University（南昌航空大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；RIKEN R-CCS（理化学研究所计算科学研究中心）；NVIDIA（英伟达）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Technical University of Munich（慕尼黑工业大学）
Low Density Parity Check (LDPC) codes are linear error correcting codes used in communication systems for Forward Error Correction (FEC). But, intensive computation is required for encoding and decoding of LDPC codes, making it difficult for practical usage in general purpose software based signal processing systems. In order to accelerate the encoding and decoding of LDPC codes, distributed processing over multiple multi-core CPUs using Message Passing Interface (MPI) is performed. Implementation is done using Stream Processing and Batch Processing mechanisms and the execution time for both i…
Many extreme scale scientific applications have workloads comprised of a large number of individual high-performance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Python-based Pilot system. We describe RP's design, architecture and implementation. We characterize its performance and show its a…
Many extreme scale scientific applications have workloads comprised of a large number of individual high-performance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible pilot-enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show it…
QSimAdv: A Late-Bound, Vendor-Agnostic Architecture for High-Performance Quantum-Circuit Simulation
Pawsey Supercomputing Research Centre（Pawsey超级计算研究中心）；The University of Western Australia（西澳大学）；Nanjing University of Information Science & Technology（南京信息工程大学）；Sina Weibo（新浪微博）；Beijing Zhongke Liangshu Technology Co., Ltd（北京中科良数科技有限公司）
