Misplaced Pages

#787212

65-451: The scope of this standard is to offer recommendations for the implementation of middleware . It deals with the following topics: Among existing middleware and application programming approaches, one simple, powerful, and flexible approach consists in using servers available in different administrative domains through the classical client-server or Remote Procedure Call (RPC) paradigm. Network Enabled Servers (NES) implement this model, which

130-434: A parallel program running on a distributed memory system. Actual distributed memory supercomputers such as computer clusters often run such programs. The principal MPI-1 model has no shared memory concept, and MPI-2 has only a limited distributed shared memory concept. Nonetheless, MPI programs are regularly run on shared memory computers, and both MPICH and Open MPI can use shared memory for message transfer if it

195-561: A client since (1) the size of the data used in grid applications may be large and useless data transfers must be avoided; (2) data are not always stored on the client side but may be made available either on a storage resource or within the GridRPC platform. Hence, a side effect is that a fully GridRPC-compliant code can be written and compiled with any GridRPC middleware implementing the GridRPC Data Management API. The GridRPC model

260-542: A draft in November 2002 and which is an Open Grid Forum (OGF) standard since September 2007. Thus a GridRPC source code which does not involve specific middleware data can be compiled and executed with any GridRPC compliant middleware. Due to the difference in the choice of implementation of the GridRPC API, a document describing the interoperability between GridRPC middleware has also been written. Its main goals are to describe

325-461: A general index was added. All valid MPI-3.0 programs are also valid in MPI-3.1. MPI-4.0 is a major update that introduces large-count versions of many routines, persistent collective operations, partitioned communications, and a new MPI initialization method. It also adds application info assertions and improves error handling definitions, along with various smaller enhancements. Any valid MPI-3.1 program

390-485: A library from one family works as a drop-in replacement of one from the same family, but direct replacement across families is impossible. The French CEA maintains a wrapper interface to facilitate such switches. MPI hardware research focuses on implementing MPI directly in hardware, for example via processor-in-memory , building MPI operations into the microcircuitry of the RAM chips in each node. By implication, this approach

455-452: A message-passing application programmer interface, together with protocol and semantic specifications for how its features must behave in any implementation." MPI's goals are high performance, scalability, and portability. MPI remains the dominant model used in high-performance computing today. MPI is not sanctioned by any major standards body; nevertheless, it has become a de facto standard for communication among processes that model

520-646: A multi-core machine) will be assigned just a single process. This assignment happens at runtime through the agent that starts the MPI program, normally called mpirun or mpiexec. MPI library functions include, but are not limited to, point-to-point rendezvous-type send/receive operations, choosing between a Cartesian or graph -like logical process topology, exchanging data between process pairs (send/receive operations), combining partial results of computations (gather and reduce operations), synchronizing nodes (barrier operation) as well as obtaining network-related information such as

585-438: A number of instances of the named MPI process. The newly spawned set of MPI processes form a new MPI_COMM_WORLD intracommunicator but can communicate with the parent and the intercommunicator the function returns. MPI_Comm_spawn_multiple is an alternate interface that allows the different instances spawned to be different binaries with different arguments. The parallel I/O feature is sometimes called MPI-IO, and refers to

650-534: A process group (which can mean the entire process pool or a program-defined subset). A typical function is the MPI_Bcast call (short for " broadcast "). This function takes data from one node and sends it to all processes in the process group. A reverse operation is the MPI_Reduce call, which takes data from all processes in a group, performs an operation (such as summing), and stores the results on one node. MPI_Reduce

715-413: A set of functions designed to abstract I/O management on distributed systems to MPI, and allow files to be easily accessed in a patterned way using the existing derived datatype functionality. The little research that has been done on this feature indicates that it may not be trivial to get high performance gains by using MPI-IO. For example, an implementation of sparse matrix-vector multiplications using

SECTION 10

#1732876152788

780-519: A write to remote memory, a read from remote memory, and a reduction operation on the same memory across a number of tasks, respectively. Also defined are three different methods to synchronize this communication (global, pairwise, and remote locks) as the specification does not guarantee that these operations have taken place until a synchronization point. These types of call can often be useful for algorithms in which synchronization would be inconvenient (e.g. distributed matrix multiplication ), or where it

845-413: Is MPI_Send , which allows one specified process to send a message to a second specified process. Point-to-point operations, as these are called, are particularly useful in patterned or irregular communication, for example, a data-parallel architecture in which each processor routinely swaps regions of data with specific other processors between calculation steps, or a master–slave architecture in which

910-454: Is a popular distributed environment and message passing system developed in 1989, and which was one of the systems that motivated the need for standard parallel message passing. Threaded shared memory programming models (such as Pthreads and OpenMP ) and message passing programming (MPI/PVM) can be considered complementary and have been used together on occasion in, for example, servers with multiple large shared-memory nodes. The MPI interface

975-470: Is also called GridRPC. Clients submit computation requests to a resource broker whose goal is to find a server available on the Grid. Scheduling is frequently applied to balance the work among the servers and a list of available servers is sent back to the client; the client is then able to send the data and the request to one of the suggested servers to solve its problem. Thanks to the growth of network bandwidth and

1040-498: Is available. Designing programs around the MPI model (contrary to explicit shared memory models) has advantages when running on NUMA architectures since MPI encourages memory locality . Explicit shared memory programming was introduced in MPI-3. Although MPI belongs in layers 5 and higher of the OSI Reference Model , implementations may cover most layers, with sockets and Transmission Control Protocol (TCP) used in

1105-523: Is compatible with MPI-4.0. MPI-4.1 is a minor update focused on corrections and clarifications to the MPI-4.0 standard. It deprecates several routines, the MPI_HOST attribute key, and the mpif.h Fortran include file. A new routine has been added to inquire about the hardware running the MPI program. Any valid MPI-4.0 program remains valid in MPI-4.1. MPI is often compared with Parallel Virtual Machine (PVM), which

1170-558: Is desirable for tasks to be able to balance their load while other processors are operating on data. The key aspect is "the ability of an MPI process to participate in the creation of new MPI processes or to establish communication with MPI processes that have been started separately." The MPI-2 specification describes three main interfaces by which MPI processes can dynamically establish communications, MPI_Comm_spawn , MPI_Comm_accept / MPI_Comm_connect and MPI_Comm_join . The MPI_Comm_spawn interface allows an MPI process to spawn

1235-549: Is equivalent to having each process (including the root itself) call MPI_Send and the root make the corresponding number of ordered MPI_Recv calls to assemble all of these arrays into a larger one: However, you may instead wish to send data as one block as opposed to 100 int s. To do this define a "contiguous block" derived data type: For passing a class or a data structure, MPI_Type_create_struct creates an MPI derived data type from MPI_predefined data types, as follows: where: The disp (displacements) array

1300-502: Is in principle optimized for the hardware on which it runs). MPI uses Language Independent Specifications (LIS) for calls and language bindings. The first MPI standard specified ANSI C and Fortran-77 bindings together with the LIS. The draft was presented at Supercomputing 1994 (November 1994) and finalized soon thereafter. About 128 functions constitute the MPI-1.3 standard which was released as

1365-423: Is independent of language, operating system, and CPU, but cannot be readily updated or removed. Another approach has been to add hardware acceleration to one or more parts of the operation, including hardware processing of MPI queues and using RDMA to directly transfer data between memory and the network interface controller without CPU or OS kernel intervention. mpicc (and similarly mpic++ , mpif90 , etc.)

SECTION 20

#1732876152788

1430-462: Is meant to provide essential virtual topology, synchronization , and communication functionality between a set of processes (that have been mapped to nodes/servers/computer instances) in a language-independent way, with language-specific syntax (bindings), plus a few language-specific features. MPI programs always work with processes, but programmers commonly refer to the processes as processors. Typically, for maximum performance, each CPU (or core in

1495-431: Is most commonly used for software that enables communication and management of data in distributed applications . An IETF workshop in 2000 defined middleware as "those services found above the transport (i.e. over TCP/IP) layer set of services but below the application environment" (i.e. below application-level APIs ). In this more specific sense middleware can be described as the hyphen ("-") in client-server , or

1560-419: Is needed for data structure alignment , since the compiler may pad the variables in a class or data structure. The safest way to find the distance between different fields is by obtaining their addresses in memory. This is done with MPI_Get_address , which is normally the same as C's & operator but that might not be true when dealing with memory segmentation . Passing a data structure as one block

1625-405: Is often useful at the start or end of a large distributed calculation, where each processor operates on a part of the data and then combines it into a result. Other operations perform more sophisticated tasks, such as MPI_Alltoall which rearranges n items of data such that the n th node gets the n th item of data from each. Many MPI functions require that you specify the type of data which

1690-400: Is pictured in the following figure. Here is how communications are handled: (1) servers register their services to a registry; (2) when a client needs the execution of a service, it contacts the registry and (3) the registry returns a handle to the client; (4) then the client uses the handle to invoke the service on the server and (5) eventually receives back the results. Mechanisms involved in

1755-416: Is sent between processes. This is because MPI aims to support heterogeneous environments where types might be represented differently on the different nodes (for example they might be running different CPU architectures that have different endianness ), in which case MPI implementations can perform data conversion . Since the C language does not allow a type itself to be passed as a parameter, MPI predefines

1820-399: Is significantly faster than passing one item at a time, especially if the operation is to be repeated. This is because fixed-size blocks do not require serialization during transfer. Given the following data structures: Here's the C code for building an MPI-derived data type: MPI-2 defines three one-sided communications operations, MPI_Put , MPI_Get , and MPI_Accumulate , being

1885-474: Is somewhat standardized through the Service Availability Forum and is commonly used in complex, embedded systems within the telecom, defence, and aerospace industries. Many categories of middleware have been defined, based on the field in which it is used or the application module it serves. A recent bibliography identified the main categories of middleware as follows: The term middleware

1950-407: Is the type of variables representing a remote function bound to a given server. Once allocated by the client, such a variable can be used to launch the service as many times as desired. It is explicitly invalidated by the user when not needed anymore; (2) grpc_session_t is the type of variables used to identify a specific non-blocking GridRPC call. Such a variable is mandatory to obtain information on

2015-438: Is used in other contexts as well. Middleware is sometimes used in a similar sense to a software driver , an abstraction layer that hides detail about hardware devices or other software from an application. Message Passing Interface The Message Passing Interface ( MPI ) is a standardized and portable message-passing standard designed to function on parallel computing architectures . The MPI standard defines

GridRPC - Misplaced Pages Continue

2080-549: The -to- in peer-to-peer . Middleware includes web servers , application servers , content management systems , and similar tools that support application development and delivery. ObjectWeb defines middleware as: "The software layer that lies between the operating system and applications on each side of a distributed computing system in a network." Services that can be regarded as middleware include enterprise application integration , data integration , message oriented middleware (MOM), object request brokers (ORBs), and

2145-673: The SAGA interface from the OGF, and without the standardized API calls, like OmmiRPC, XtremWeb. The RPC model over the internet has also been used for several applications. Transparently through the Internet, large optimization problems can be solved using different approaches by simply filling a web page for remote image processing computations, the use of mathematical libraries or studies on heuristics and resolution methods for sparse linear algebra like GridTLSE. This approach of providing computation services through

2210-557: The enterprise service bus (ESB). Database access services are often characterised as middleware. Some of them are language specific implementations and support heterogeneous features and other related communication features. Examples of database-oriented middleware include ODBC , JDBC , and transaction processing monitors. Distributed computing system middleware can loosely be divided into two categories—those that provide human-time services (such as web request servicing) and those that perform in machine-time. This latter middleware

2275-506: The locking and disk seek overhead. Due to its vast performance benefits, MPI-IO also became the underlying I/O layer for many state-of-the-art I/O libraries, such as HDF5 and Parallel NetCDF . Its popularity also triggered research on collective I/O optimizations, such as layout-aware I/O and cross-file aggregation. Many other efforts are derivatives of MPICH, LAM, and other works, including, but not limited to, commercial implementations from HPE , Intel , Microsoft , and NEC . While

2340-409: The operating system . It can be described as "software glue". Middleware makes it easier for software developers to implement communication and input/output, so they can focus on the specific purpose of their application. It gained popularity in the 1980s as a solution to the problem of how to link newer applications to older legacy systems, although the term had been in use since 1968. The term

2405-421: The syntax and semantics of library routines that are useful to a wide range of users writing portable message-passing programs in C , C++ , and Fortran . There are several open-source MPI implementations , which fostered the development of a parallel software industry , and encouraged development of portable and scalable large-scale parallel applications. The message passing interface effort began in

2470-525: The API must provide means to make synchronous and/or asynchronous calls to a service. If the latter, clients must also be able to wait in a blocking or non-blocking manner after the completion of a given service. This naturally involves some data structures and conducts to a rigorous definition of the functions of the API. GridRPC Data Types Three main data types are needed to implement the API: (1) grpc_function_handle_t

2535-407: The GridRPC environment ready and finish it. In order to initialize and destruct a function handle, grpc_function_handle_init() and grpc_function_handle_destruct() functions have to be called. Because a function handle can be dynamically associated to a server, because of resource discovery mechanisms for example, a call to grpc_function_handle_default() let to postpone the server selection until

2600-515: The Internet is also highly close to the Service Oriented Computing (SOA) paradigm, and is the core of the Cloud computing . One simple, yet effective, mean to execute jobs on a computing grid is to use a GridRPC middleware, which relies on the GridRPC paradigm. For each request, the GridRPC middleware manages the management of the submission, of the input and output data, of the execution of

2665-477: The MPI I/O library shows a general behavior of minor performance gain, but these results are inconclusive. It was not until the idea of collective I/O implemented into MPI-IO that MPI-IO started to reach widespread adoption. Collective I/O substantially boosts applications' I/O bandwidth by having processes collectively transform the small and noncontiguous I/O operations into large and contiguous ones, thereby reducing

GridRPC - Misplaced Pages Continue

2730-952: The MPI session. Each communicator gives each contained process an independent identifier and arranges its contained processes in an ordered topology . MPI also has explicit groups, but these are mainly good for organizing and reorganizing groups of processes before another communicator is made. MPI understands single group intracommunicator operations, and bilateral intercommunicator communication. In MPI-1, single group operations are most prevalent. Bilateral operations mostly appear in MPI-2 where they include collective communication and dynamic in-process management. Communicators can be partitioned using several MPI commands. These commands include MPI_COMM_SPLIT , where each process joins one of several colored sub-communicators by declaring itself to have that color. A number of important MPI functions involve communication between two specific processes. A popular example

2795-684: The MPI-2 standard. MPI-3.0 introduces significant updates to the MPI standard, including nonblocking versions of collective operations, enhancements to one-sided operations, and a Fortran 2008 binding. It removes deprecated C++ bindings and various obsolete routines and objects. Importantly, any valid MPI-2.2 program that avoids the removed elements is also valid in MPI-3.0. MPI-3.1 is a minor update focused on corrections and clarifications, particularly for Fortran bindings. It introduces new functions for manipulating MPI_Aint values, nonblocking collective I/O routines, and methods for retrieving index values by name for MPI_T performance variables. Additionally,

2860-555: The U.S. National Science Foundation (NSF) under grant ASC-9310330, NSF Science and Technology Center Cooperative agreement number CCR-8809615, and from the European Commission through Esprit Project P6643. The University of Tennessee also made financial contributions to the MPI Forum. MPI is a communication protocol for programming parallel computers . Both point-to-point and collective communication are supported. MPI "is

2925-515: The United States and Europe. Most of the major vendors of concurrent computers were involved in the MPI effort, collaborating with researchers from universities, government laboratories, and industry . MPI provides parallel hardware vendors with a clearly defined base set of routines that can be efficiently implemented. As a result, hardware vendors can build upon this collection of standard low-level routines to create higher-level routines for

2990-472: The actual call is made on the handle. grpc_get_handle() let the client retrieve the function handle corresponding to a session ID ( e.g., to a non-blocking call) that has been previously performed. Depending on the type of the call, blocking or non-blocking, the client can use the grpc_call() and grpc_call_async() function. If the latter, the client possesses after the call a session ID which can be used to respectively probe or wait for completion, cancel

3055-538: The call and check the error status of a non-blocking call. After issuing a unique or numerous non-blocking calls, a client can use: grpc_probe() to know if the execution of the service has completed; grpc_probe_or() to know if one of the previous non-blocking calls has completed; grpc_cancel() to cancel a call; grpc_wait() to block until the completion of the requested service; grpc_wait_and() to block until all services corresponding to session IDs used as parameters are finished; grpc_wait_or() to block until any of

3120-406: The constants MPI_INT , MPI_CHAR , MPI_DOUBLE to correspond with int , char , double , etc. Here is an example in C that passes arrays of int s from all processes to one. The one receiving process is called the "root" process, and it can be any designated process but normally it will be process 0. All the processes ask to send their arrays to the root with MPI_Gather , which

3185-461: The difference in behaviour of the GridRPC middleware and to propose a common test that all GridRPC middleware must pass. Discussions have then been undertaken on the data management within GridRPC middleware. A draft of an API has been proposed during the OGF'21 in October 2007. The motivation for this document is to provide explicit functions to manipulate the data exchange between a GridRPC platform and

3250-427: The distributed-memory communication environment supplied with their parallel machines . MPI provides a simple-to-use portable interface for the basic user, yet one powerful enough to allow programmers to use the high-performance message passing operations available on advanced machines. In an effort to create a universal standard for message passing, researchers did not base it off of a single system but it incorporated

3315-454: The final end of the MPI-1 series in 2008. At present, the standard has several versions: version 1.3 (commonly abbreviated MPI-1 ), which emphasizes message passing and has a static runtime environment, MPI-2.2 (MPI-2), which includes new features such as parallel I/O, dynamic process management and remote memory operations, and MPI-3.1 (MPI-3), which includes extensions to the collective operations with non-blocking versions and extensions to

SECTION 50

#1732876152788

3380-549: The interface. It is relatively easy to write multithreaded point-to-point MPI code, and some implementations support such code. Multithreaded collective communication is best accomplished with multiple copies of Communicators, as described below. MPI provides several features. The following concepts provide context for all of those abilities and help the programmer to decide what functionality to use in their application programs. Four of MPI's eight basic concepts are unique to MPI-2. Communicator objects connect groups of processes in

3445-411: The job on the remote resource, etc. To make available a service, a programmer must implement two codes: a client, where data are defined and which is run by the user when requesting the service, and a server, which contains the implementation of the service which is executed on the remote resource. One step to ease the development of such codes conducted to define a GridRPC API, which has been proposed as

3510-401: The master sends new task data to a slave whenever the prior task is completed. MPI-1 specifies mechanisms for both blocking and non-blocking point-to-point communication mechanisms, as well as the so-called 'ready-send' mechanism whereby a send request can be made only when the matching receive request has already been made. Collective functions involve communication among all processes in

3575-510: The most useful features of several systems, including those designed by IBM, Intel , nCUBE , PVM, Express, P4 and PARMACS. The message-passing paradigm is attractive because of wide portability and can be used in communication for distributed-memory and shared-memory multiprocessors, networks of workstations, and a combination of these elements. The paradigm can apply in multiple settings, independent of network speed or memory architecture. Support for MPI meetings came in part from DARPA and from

3640-719: The number of processes in the computing session, current processor identity that a process is mapped to, neighboring processes accessible in a logical topology, and so on. Point-to-point operations come in synchronous , asynchronous , buffered, and ready forms, to allow both relatively stronger and weaker semantics for the synchronization aspects of a rendezvous-send. Many outstanding operations are possible in asynchronous mode, in most implementations. MPI-1 and MPI-2 both enable implementations that overlap communication and computation, but practice and theory differ. MPI also specifies thread safe interfaces, which have cohesion and coupling strategies that help avoid hidden state within

3705-509: The one-sided operations. MPI-2's LIS specifies over 500 functions and provides language bindings for ISO C , ISO C++ , and Fortran 90 . Object interoperability was also added to allow easier mixed-language message passing programming. A side-effect of standardizing MPI-2, completed in 1996, was clarifying the MPI-1 standard, creating the MPI-1.2. MPI-2 is mostly a superset of MPI-1, although some functions have been deprecated. MPI-1.3 programs still work under MPI implementations compliant with

3770-473: The reduction of network latency, small computation requests can now be sent to servers available on the Grid. To make effective use of today's scalable resource platforms, it is important to ensure scalability in the middleware layers as well. This service-oriented approach is not new. Several research projects have targeted this paradigm in the past. The main middleware implementing the API are DIET, NetSolve/GridSolve, Ninf, but some other environments use it like

3835-480: The service corresponding to session IDs used as parameters has finished; grpc_wait_all() to block until all non-blocking calls have completed; and grpc_wait_any() to wait until any previously issued non-blocking request has completed. Talk about the lib (+link) against which a code must compile and give a basic example Middleware Middleware is a type of computer software program that provides services to software applications beyond those available from

3900-507: The specifications mandate a C and Fortran interface, the language used to implement MPI is not constrained to match the language or languages it seeks to support at runtime. Most implementations combine C, C++ and assembly language, and target C, C++, and Fortran programmers. Bindings are available for many other languages, including Perl, Python, R, Ruby, Java, and CL (see #Language bindings ). The ABI of MPI implementations are roughly split between MPICH and Open MPI derivatives, so that

3965-456: The standardization process. Jack Dongarra , Tony Hey , and David W. Walker put forward a preliminary draft proposal, "MPI1", in November 1992. In November 1992 a meeting of the MPI working group took place in Minneapolis and decided to place the standardization process on a more formal footing. The MPI working group met every 6 weeks throughout the first 9 months of 1993. The draft MPI standard

SECTION 60

#1732876152788

4030-491: The status of a job, in order for a client to wait after, cancel or know the error status of a call; (3) grpc_error_t groups all kind of errors and returns status codes involved in the GridRPC API. GridRPC Functions grpc_initialize() and grpc_finalize() functions are similar to the MPI initialize and finalize calls. It is mandatory that any GridRPC call is performed in between these two calls. They read configuration files, make

4095-524: The summer of 1991 when a small group of researchers started discussions at a mountain retreat in Austria. Out of that discussion came a Workshop on Standards for Message Passing in a Distributed Memory Environment, held on April 29–30, 1992 in Williamsburg, Virginia . Attendees at Williamsburg discussed the basic features essential to a standard message-passing interface and established a working group to continue

4160-435: The transport layer. Most MPI implementations consist of a specific set of routines directly callable from C , C++ , Fortran (i.e., an API) and any language able to interface with such libraries, including C# , Java or Python . The advantages of MPI over older message passing libraries are portability (because MPI has been implemented for almost every distributed memory architecture) and speed (because each implementation

4225-540: Was presented at the Supercomputing '93 conference in November 1993. After a period of public comments, which resulted in some changes in MPI, version 1.0 of MPI was released in June 1994. These meetings and the email discussion together constituted the MPI Forum, membership of which has been open to all members of the high-performance-computing community. The MPI effort involved about 80 people from 40 organizations, mainly in

#787212