International Business Machines Corporation

Globalfoundries Inc.

Samuel J Miller

Thomas M Gooding

Mark Gregory Megerian

Michael E Aho

Michael B Mundy

John Eric Attinella

Brant L Knudson

Thomas A Budnik

William Maupin Stockdell

Michael K Gschwind

Valentina Salapura

Dong Chen

Paul William Coteus

Michael A Blocksome

Alan G Gara

Joseph D Ratterman

Philip Heidelberger

Adam James Muff

Todd Edward Takken

Martin Ohmacht

Bruce Martin Fleischer

Alexandre E Eichenberger

Robert William Wisniewski

John A Gunnels

Mark Edwin Giampapa

Burkhard Steinmacher-Burow

Kathryn Mary O'Brien

Sameh W Asaad

Matthias Augustin Blumrich

Shawn Anthony Hall

John Kevin O'Brien

Thomas Winters Fox

Chen-Yong Cher

Barry Marshall Trager

Peng Wu

Charles David Wait

Sameer Kumar

James Anthony Marcella

George L Chiu

Todd Alan Inglett

Krishnan Sugavanam

Gerard Vincent Kopcsay

Jeffrey John Parker

Robert Matthew Senger

Noel A Eisley

Douglas R Miller

Craig Brian Stunkel

Rudolf Adriaan Haring

David L Satterfield

Yutaka Sugawara

Amith R Mamidala

Kristan D Davis

Jose R Brunheroto

Matthew Roger Ellavsky

Robert E Walkup

James Lee Van Oosten

Ruth Janine Poole

Gabor J Dozsa

Ralph E Bellofatto

Peter Boyle

Norman Christ

Paul V Allen

Kahn C Evans

Alfred Thomas Watson, Iii

Brian E Smith

Jeffrey Wayne Tenner

Alwood Patrick Williams, Iii

Lynnette Elaine Carston

Gary E Rohret

Techniques are disclosed for loading programs efficiently in a parallel computing system. In one embodiment, nodes of the parallel computing system receive a load description file which indicates, for each program of a multiple program multiple data (MPMD) job, nodes which are to load the program. The nodes determine, using collective operations, a total number of programs to load and a number of programs to load in parallel. The nodes further generate a class route for each program to be loaded in parallel, where the class route generated for a particular program includes only those nodes on which the program needs to be loaded. For each class route, a node is selected using a collective operation to be a load leader which accesses a file system to load the program associated with a class route and broadcasts the program via the class route to other nodes which require the program.

Collectively loading programs in a multiple program multiple data environment

A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaflop-scale includes node architectures based upon System-On-a-Chip technology, where each processing node comprises a single Application Specific Integrated Circuit (ASIC). The ASIC nodes are interconnected by a five dimensional torus network that optimally maximize the throughput of packet communications between nodes and minimize latency. The network implements collective network and a global asynchronous network that provides global barrier and notification functions. Integrated in the node design include a list-based prefetcher. The memory system implements transaction memory, thread level speculation, and multiversioning cache that improves soft error rate at the same time and supports DMA functionality allowing for parallel processing message-passing.

Multi-petascale highly efficient parallel supercomputer

Collectively loading an application in a parallel computer, the parallel computer comprising a plurality of compute nodes, including: identifying, by a parallel computer control system, a subset of compute nodes in the parallel computer to execute a job; selecting, by the parallel computer control system, one of the subset of compute nodes in the parallel computer as a job leader compute node; retrieving, by the job leader compute node from computer memory, an application for executing the job; and broadcasting, by the job leader to the subset of compute nodes in the parallel computer, the application for executing the job.

Collectively loading an application in a parallel computer

A Multi-Petascale Highly Efficient Parallel Supercomputer of 100 petaOPS-scale computing, at decreased cost, power and footprint, and that allows for a maximum packaging density of processing nodes from an interconnect point of view. The Supercomputer exploits technological advances in VLSI that enables a computing model where many processors can be integrated into a single Application Specific Integrated Circuit (ASIC). Each ASIC computing node comprises a system-on-chip ASIC utilizing four or more processors integrated into one die, with each having full access to all system resources and enabling adaptive partitioning of the processors to functions such as compute or messaging I/O on an application by application basis, and preferably, enable adaptive partitioning of functions in accordance with various algorithmic phases within an application, or if I/O or other processors are underutilized, then can participate in computation or communication nodes are interconnected by a five dimensional torus network with DMA that optimally maximize the throughput of packet communications between nodes and minimize latency.

A method, computer program product and computer system for scaling and managing requests on a massively parallel machine, such as one running in MIMD mode on a SIMD machine. A submit mux (multiplexer) is used to federate work requests and to forward the requests to the management node. A resource arbiter receives and manges these work requests. A MIMD job controller works with the resource arbiter to manage the work requests on the SIMD partition. The SIMD partition may utilize a mux of its own to federate the work requests and the computer nodes. Instructions are also provided to control and monitor the work requests.

Scaling and managing work requests on a massively parallel machine

Embodiments of the invention provide a method of optimizing a query, including determining an execution plan for use in executing the query, based on a monetary cost of the energy needed to execute the query. A query optimization component may determine a plurality of execution plans for the query. The query optimization component may then select one of the plans to use in executing the query based on the monetary cost calculated for the plan.

Energy monetary cost aware query optimization

A method for resource management on a computer system utilizing hardware and environmental information. A caller interacts with an application program interface to handle information requests with a persistent data storage device to combine information involving hardware resource information, environmental data and other system information, all both historical, present and predicted values. Application execution decisions may then made regarding hardware for the calling entity. The method may be implemented as a computer process.

Resource management on a computer system utilizing hardware and environmental factors

Methods, systems, and products for dynamically reassigning a connected node to a block of compute nodes for re-launching a failed job that include: identifying that a job failed to execute on the block of compute nodes because connectivity failed between a compute node assigned as at least one of the connected nodes for the block of compute nodes and its supporting I/O node; and re-launching the job, including selecting an alternative connected node that is actively coupled for data communications with an active I/O node; and assigning the alternative connected node as the connected node for the block of compute nodes running the re-launched job.

Dynamically reassigning a connected node to a block of compute nodes for re-launching a failed job

Growing community of inventors

Rochester, MN, United States of America

Samuel J Miller