Collective operations are an important aspect of the currently most important message-passing programming model MPI (Message Passing Interface). Many MPI applications make heavy use of collective operations. Collective operations involve the active participation of a known group of processes and are usually implemented on top of MPI point-to-point message passing. Many optimizations of the used...

The Message Passing Interface (MPI) is widely used to write parallel programs using message passing. MARMOT is a tool to aid in the development and debugging of MPI programs. This paper presents the situations where incorrect usage of MPI by the application programmer is automatically detected. Examples are the introduction of irreproducibility, deadlocks and incorrect management of resources l...

The diverse message passing interfaces provided on parallel and distributed computing systems have caused diiculty in movement of application software from one system to another and have inhibited the commercial development of tools and libraries for these systems. The Message Passing Interface (MPI) Forum has developed a de facto interface standard which was nalised in Q1 of 1994. Major parall...

The last several years saw an emergence of standardization activities for real-time systems including standardization of operating systems (series of POSIX standards [1]), of communication for distributed (POSIX.21 [15]) and parallel systems (MPI/RT [6] and real-time object management (real-time CORBA [14]). This article describes the ongoing work of real-time message passing interface (MPI/RT)...

This paper describes current activities of the MPI-2 Forum. The MPI-2 Forum is a group of parallel computer vendors, library writers, and application specialists working together to de ne a set of extensions to MPI (Message Passing Interface). MPI was de ned by the same process and now has many implementations, both vendor-proprietary and publicly available, for a wide variety of parallel compu...

MPI (Message Passing Interface) is a proposed message passing standard for development of efficient and portable parallel programs. An implementation of MPI is presented and evaluated for the Meiko CS/2, a 64 node parallel computer, and a network of 8 SGI workstations connected by an ATM switch and Ethernet.

The Message Passing Interface (MPI) has become a standard for message passing parallel applications. This report first introduces the underlying paradigm, message passing, and explores some of the challenges explicit message passing poses for developing parallel programs. We then take a closer look at the MPI standardization effort, its goals, and its results to see what features the current ve...

The Message Passing Interface (MPI) was introduced in June 1994 as a standard message passing API for parallel scientific computing. The original MPI standard had language bindings for Fortran, C and C++. A new generation of distributed, Internet-enabled computing inspired the later introduction of similar message passing APIs for Java [1][2]. Current implementations of MPI for Java usually fol...

MPI for Python (mpi4py) has evolved to become the most used binding message passing interface (MPI). We report on various improvements and features that mpi4py gradually accumulated over past decade, including support up MPI-3.1 specification, CUDA-aware implementations, other utilities at intersection of MPI-based parallel distributed computing application development.

We describe a number of early e orts to make use of the Message Passing Interface (MPI) standard in applications, based on an informal survey conducted in May-June, 1994. Rather than a de nitive statement of all MPI development work, this paper addresses initial successes, progress, and impressions that application developers have with MPI, according to the responses received. We summarize the ...