Index

Abstract processors in HPF

7.3.1 Processors

Actor model

Agglomeration

2.1 Methodical Design, (, )

and granularity: 2.4.1 Increasing Granularity, Surface-to-Volume Effects.
and granularity: 2.4.1 Increasing Granularity, Surface-to-Volume Effects.
design checklist: 2.4.4 Agglomeration Design Checklist
for atmosphere model: Agglomeration.
for floorplan optimization: Agglomeration.
for Fock matrix problem: Communication and Agglomeration.
in data-parallel model: 7.1.3 Design

AIMS performance tool

9.4.7 AIMS, Chapter Notes

Amdahl's law

application to HPF: 7.7.2 Sequential Bottlenecks
definition: 3.2.1 Amdahl's Law, Chapter Notes
definition: 3.2.1 Amdahl's Law, Chapter Notes

Applied Parallel Research

ARPANET

2.3.4 Asynchronous Communication

Asymptotic analysis

limitations of: 3.2.3 Asymptotic Analysis, 3.2.3 Asymptotic Analysis
limitations of: 3.2.3 Asymptotic Analysis, 3.2.3 Asymptotic Analysis
reference: Chapter Notes

Asynchronous communication

in CC++: 5.6 Asynchronous Communication
in FM: 6.5 Asynchronous Communication
in MPI: 8.4 Asynchronous Communication

Asynchronous Transfer Mode

Atmosphere model

basic equations: 2.6.1 Atmosphere Model Background
description: (, )
description: (, )
parallel algorithms: (, )
parallel algorithms: (, )
references: Chapter Notes

BBN Butterfly

Bisection bandwidth

Bisection width

Exercises, Chapter Notes

Bitonic mergesort

7.7.2 Sequential Bottlenecks

Bottlenecks in HPF

Branch-and-bound search

description: 2.7.1 Floorplan Background, Chapter Notes
description: 2.7.1 Floorplan Background, Chapter Notes
in MPI: 8.1 The MPI Programming

Breadth-first search

Partition.

Bridge construction problem

definition: 1.3.1 Tasks and Channels
determinism: 1.3.1 Tasks and Channels
in CC++: 5.2 CC++ Introduction
in Fortran M: 6.1 FM Introduction, 6.1 FM Introduction, 6.4.3 Dynamic Channel Structures
in Fortran M: 6.1 FM Introduction, 6.1 FM Introduction, 6.4.3 Dynamic Channel Structures
in Fortran M: 6.1 FM Introduction, 6.1 FM Introduction, 6.4.3 Dynamic Channel Structures
in MPI: 8.2 MPI Basics

Bubblesort

Bucketsort

6.5 Asynchronous Communication

Bus-based networks

Bus-based Networks.

Busy waiting strategy

Butterfly

bandwidth competition on: Multistage Interconnection Networks.
description: Replicating Computation.
hypercube formulation: Hypercube Network.

C*

Chapter Notes, 7 High Performance Fortran, Chapter Notes

C++

1.2.2 Other Machine Models, Bus-based Networks.

classes: 5.1.2 Classes
constructor functions: 5.1.2 Classes
default constructors: 5.1.2 Classes
inheritance: 5.1.3 Inheritance, 5.1.3 Inheritance
inheritance: 5.1.3 Inheritance, 5.1.3 Inheritance
member functions: 5.1.2 Classes
overloading: 5.1.1 Strong Typing and
protection: 5.1.2 Classes
virtual functions: 5.1.3 Inheritance

Cache effect

3.6.2 Speedup Anomalies

Cache memory

CC++

Part II: Tools

asynchronous communication: 5.6 Asynchronous Communication
basic abstractions: 5.2 CC++ Introduction
channel communication: 5.5.2 Synchronization
communication costs: 5.10 Performance Issues, 5.10 Performance Issues
communication costs: 5.10 Performance Issues, 5.10 Performance Issues
communication structures: 5.5 Communication
compiler optimization: 5.10 Performance Issues
concurrency: 5.3 Concurrency
library building: 5.11 Case Study: Channel
locality: 5.4 Locality
mapping: (, )
mapping: (, )
mapping: (, )
mapping: (, )
modularity: 5.9 Modularity
modularity: 5.9 Modularity
modularity: 5.9 Modularity
modularity: 5.9 Modularity
nondeterministic interactions: 5.7 Determinism
sequential composition: 5.9 Modularity, 5.9 Modularity
sequential composition: 5.9 Modularity, 5.9 Modularity
synchronization mechanisms: 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.3 Mutual Exclusion
synchronization mechanisms: 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.3 Mutual Exclusion
synchronization mechanisms: 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.3 Mutual Exclusion
synchronization mechanisms: 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.2 Synchronization, 5.5.3 Mutual Exclusion
threads: 5.2 CC++ Introduction
tutorial: Chapter Notes
unstructured parallelism: 5.3 Concurrency

CHAMMP climate modeling program

Channels

3.8 Input/Output, Chapter Notes

and data dependencies: 1.3.1 Tasks and Channels
connecting outport/inport pairs: 1.3.1 Tasks and Channels
creation in Fortran M: 6.3.1 Creating Channels
dynamic in Fortran M: 6.4.3 Dynamic Channel Structures
for argument passing in Fortran M: 6.7 Argument Passing
in communication: 2.3 Communication
in CSP: Chapter Notes

Checkpointing

CHIMP

Classes in C++

1.1.1 Trends in Applications, 2.2.2 Functional Decomposition, 2.6 Case Study: Atmosphere , 9.4.1 Paragraph

Climate modeling

in CC++: 5.8.2 Mapping Threads to
in Fortran M: 6.8.3 Submachines
in MPI: 8.8 Case Study: Earth

Clock synchronization

9.3.2 Traces, Chapter Notes

CM Fortran

Collaborative work environments

8.3 Global Operations, 9.4.2 Upshot

Collective communication

Collocation of arrays

7.3.2 Alignment

Combining scatter

7.6.3 HPF Features Not

Communicating Sequential Processes

Communication

(, )

and channels: 2.3 Communication
collective: 8.1 The MPI Programming , 8.3 Global Operations
collective: 8.1 The MPI Programming , 8.3 Global Operations
design checklist: 2.3.5 Communication Design Checklist
disadvantages of local: 2.3.2 Global Communication
for atmosphere model: Communication.
for floorplan optimization: Communication.
for Fock matrix problem: Communication and Agglomeration.
in CC++: 5.5 Communication
in data-parallel model: 7.1.3 Design
in Fortran M: 6.3 Communication
in MPI: 8.1 The MPI Programming
synchronous: 6.4.3 Dynamic Channel Structures, 8.6.2 MPI Features Not
synchronous: 6.4.3 Dynamic Channel Structures, 8.6.2 MPI Features Not

Communication costs

bandwidth competition: 3.7 A Refined Communication
in CC++: 5.10 Performance Issues
in HPF: 7.7.3 Communication Costs
in MPI: 8.7 Performance Issues, 8.7 Performance Issues
in MPI: 8.7 Performance Issues, 8.7 Performance Issues
of unaligned array mapping: 7.7.3 Communication Costs
with cyclic distribution: 7.7.3 Communication Costs

Communication patterns

2.3 Communication

asynchronous: 2.3 Communication, 2.3.4 Asynchronous Communication, 6.5 Asynchronous Communication, 7.6.2 Storage and Sequence , 8.4 Asynchronous Communication
asynchronous: 2.3 Communication, 2.3.4 Asynchronous Communication, 6.5 Asynchronous Communication, 7.6.2 Storage and Sequence , 8.4 Asynchronous Communication
asynchronous: 2.3 Communication, 2.3.4 Asynchronous Communication, 6.5 Asynchronous Communication, 7.6.2 Storage and Sequence , 8.4 Asynchronous Communication
asynchronous: 2.3 Communication, 2.3.4 Asynchronous Communication, 6.5 Asynchronous Communication, 7.6.2 Storage and Sequence , 8.4 Asynchronous Communication
asynchronous: 2.3 Communication, 2.3.4 Asynchronous Communication, 6.5 Asynchronous Communication, 7.6.2 Storage and Sequence , 8.4 Asynchronous Communication
dynamic: 2.3 Communication, 2.3.3 Unstructured and Dynamic
dynamic: 2.3 Communication, 2.3.3 Unstructured and Dynamic
local: 2.3 Communication
many-to-many: 6.4.2 Many-to-Many Communication
many-to-one: 1.4.4 Parameter Study, 6.4.1 Many-to-One Communication
many-to-one: 1.4.4 Parameter Study, 6.4.1 Many-to-One Communication
point-to-point: 8.1 The MPI Programming
static: 2.3 Communication
structured: 2.3 Communication
synchronous: 2.3 Communication, 6.4.3 Dynamic Channel Structures
synchronous: 2.3 Communication, 6.4.3 Dynamic Channel Structures
unstructured: 2.3.3 Unstructured and Dynamic , 6.4 Unstructured Communication
unstructured: 2.3.3 Unstructured and Dynamic , 6.4 Unstructured Communication

Communication time

Surface-to-Volume Effects.

Communication/computation ratio

Communicators

seeMPI

Competition for bandwidth

examples: Multistage Interconnection Networks., Multistage Interconnection Networks.
examples: Multistage Interconnection Networks., Multistage Interconnection Networks.
idealized model of: 3.7.1 Competition for Bandwidth
impact: 3.7 A Refined Communication

Compilers

data-parallel: 7.1.3 Design, 7.7.1 HPF Compilation
data-parallel: 7.1.3 Design, 7.7.1 HPF Compilation
for CC++: 5.10 Performance Issues
for Fortran M: 6.10 Performance Issues
for HPF: 7.7.1 HPF Compilation, Chapter Notes
for HPF: 7.7.1 HPF Compilation, Chapter Notes

Composition

concurrent: 4.2 Modularity and Parallel , 4.2.4 Concurrent Composition
concurrent: 4.2 Modularity and Parallel , 4.2.4 Concurrent Composition
definition: 4 Putting Components Together
parallel: 4.2 Modularity and Parallel
sequential: 4.2 Modularity and Parallel , 4.2.2 Sequential Composition
sequential: 4.2 Modularity and Parallel , 4.2.2 Sequential Composition

Compositional C++

seeCC++

Computation time

Computation Time.

Computational chemistry

2.8 Case Study: Computational , Chapter Notes

Computational geometry

1.2.2 Other Machine Models, 3.7.2 Interconnection Networks

Computer architecture

references: Chapter Notes, Chapter Notes, 12 Further Reading
references: Chapter Notes, Chapter Notes, 12 Further Reading
references: Chapter Notes, Chapter Notes, 12 Further Reading
trends: 1.1.4 Summary of Trends

Computer performance improvement

1.1.2 Trends in Computer , 1.1.2 Trends in Computer

Computer trends

1.1.4 Summary of Trends

Computer vision

Chapter Notes, 12 Further Reading

Computer-aided diagnosis

Concert C

Concurrency

explicit vs. implicit: 7.1.1 Concurrency
in CC++: 5.3 Concurrency
in data-parallel programs: 7.1.1 Concurrency
in Fortran M: 6.2 Concurrency
parallel software requirement: 1.1.2 Trends in Computer

Concurrent C

4.2 Modularity and Parallel , 4.2.4 Concurrent Composition

Concurrent composition

benefits: 4.2.4 Concurrent Composition, 4.2.4 Concurrent Composition
benefits: 4.2.4 Concurrent Composition, 4.2.4 Concurrent Composition
cost: 4.2.4 Concurrent Composition
example: 4.2.4 Concurrent Composition
in CC++: 5.8.2 Mapping Threads to
in Fortran M: 6.8.3 Submachines
tuple space example: 4.5 Case Study: Tuple

Concurrent Computation Project

Concurrent data structures

Concurrent logic programming

Conferences in parallel computing

Conformality

definition: 7.1.1 Concurrency
in Fortran M: 6.3.1 Creating Channels
of array sections: 7.2.1 Array Assignment Statement

Constructor functions in C++

Convolution algorithm

application in image processing: 4.4 Case Study: Convolution
components: 4.4.1 Components
parallel 2-D FFTs: 4.4.1 Components
parallel composition: 4.4.2 Composing Components
sequential composition: 4.4.2 Composing Components

COOL

9.1 Performance Analysis, 9.2.2 Counters

Cosmic Cube

Chapter Notes, Chapter Notes, Chapter Notes

Counters

Cray T3D

1.2.2 Other Machine Models, Chapter Notes

Crossbar switching network

Crossbar Switching Network.

Cycle time trends

1.1.2 Trends in Computer

Cyclic mapping

Cyclic Mappings., Mapping., Mapping., Chapter Notes

in HPF: 7.3.3 Distribution, 7.7.3 Communication Costs, 7.8 Case Study: Gaussian
in HPF: 7.3.3 Distribution, 7.7.3 Communication Costs, 7.8 Case Study: Gaussian
in HPF: 7.3.3 Distribution, 7.7.3 Communication Costs, 7.8 Case Study: Gaussian

Data collection

(, )

basic techniques: 9.1 Performance Analysis
counters: 9.2.2 Counters
process: 9.2.4 Summary of Data
traces: 9.2.3 Traces

Data decomposition

seeDomain decomposition

Data dependency

1.3.2 Other Programming Models, 7 High Performance Fortran

Data distribution

at module boundaries: 4.2.1 Data Distribution
dynamic: 7.6.3 HPF Features Not
in data-parallel languages: 7.1.2 Locality
in HPF: (, )
in HPF: (, )

Data distribution neutrality

benefits: 4.2.1 Data Distribution
example: (, )
example: (, )
in ScaLAPACK: 4.2.2 Sequential Composition
in SPMD libraries: Chapter Notes

Data fitting

3.5.3 Fitting Data to

Data parallelism

and Fortran 90: 7.1.4 Data-Parallel Languages, 7.2.2 Array Intrinsic Functions
and Fortran 90: 7.1.4 Data-Parallel Languages, 7.2.2 Array Intrinsic Functions
and HPF: 7.1.4 Data-Parallel Languages
and modular design: 7.1.3 Design
and task parallelism: Chapter Notes
for irregular problems: Chapter Notes
languages: 7.1.4 Data-Parallel Languages, 9.3.3 Data-Parallel Languages
languages: 7.1.4 Data-Parallel Languages, 9.3.3 Data-Parallel Languages

Data reduction

9.3.1 Profile and Counts, 9.3.2 Traces

Data replication

3.9.3 Shortest-Path Algorithms Summary

Data transformation

9.1 Performance Analysis

Data visualization

9.1 Performance Analysis, 9.3.2 Traces

Data-parallel C

7.1.4 Data-Parallel Languages, 9.3.3 Data-Parallel Languages

Data-parallel languages

Data-parallel model

1.3.2 Other Programming Models, 7.1.3 Design, 7.1.3 Design, 7.1.3 Design

Databases

Chapter Notes, Chapter Notes, 4.5.1 Application

Deadlock detection

Decision support

Dense matrix algorithms

Depth-first search

Agglomeration.

Design checklists

agglomeration: 2.4.4 Agglomeration Design Checklist
communication: 2.3.5 Communication Design Checklist
mapping: 2.5.3 Mapping Design Checklist
modular design: Design checklist.
partitioning: 2.2.3 Partitioning Design Checklist

Determinism

3.7.1 Competition for Bandwidth

advantages: 1.3.1 Tasks and Channels, Chapter Notes
advantages: 1.3.1 Tasks and Channels, Chapter Notes
in CC++: 5.7 Determinism
in Fortran M: 6.6 Determinism
in MPI: 8.2.2 Determinism

Diagonalization

Exercises, 9.4.2 Upshot

Diameter of network

Dijkstra's algorithm

3.9.2 Dijkstra's Algorithm, 3.9.3 Shortest-Path Algorithms Summary

DINO

Communication and Agglomeration.

DISCO

Distributed computing

1.1.3 Trends in Networking

Distributed data structures

Fock matrix: 2.8 Case Study: Computational
for load balancing: Decentralized Schemes.
implementation: (, )
implementation: (, )
in CC++: 5.12 Case Study: Fock
in Fortran M: 6.11 Case Study: Fock
in MPI: 8.4 Asynchronous Communication
tuple space: 4.5 Case Study: Tuple

Divide-and-conquer

Uncovering Concurrency: Divide

Domain decomposition

2.2 Partitioning, 2.2.1 Domain Decomposition

communication requirements: 2.3 Communication
for atmosphere model: 2.6 Case Study: Atmosphere
for Fock matrix problem: Partition.

Efficiency

3.3.2 Efficiency and Speedup, 3.3.2 Efficiency and Speedup, 3.3.2 Efficiency and Speedup

Embarrassingly parallel problems

1.4.4 Parameter Study

Entertainment industry

1.2.2 Other Machine Models, Chapter Notes

Environmental enquiry;tex2html_html_special_mark_quot;in MPI

8.6.2 MPI Features Not

Ethernet

performance: Communication Time., Ethernet., Multistage Interconnection Networks., Multistage Interconnection Networks.
performance: Communication Time., Ethernet., Multistage Interconnection Networks., Multistage Interconnection Networks.
performance: Communication Time., Ethernet., Multistage Interconnection Networks., Multistage Interconnection Networks.
performance: Communication Time., Ethernet., Multistage Interconnection Networks., Multistage Interconnection Networks.

Event traces

9.1 Performance Analysis, 9.3.2 Traces

Execution profile

3.4.3 Execution Profiles, 3.6 Evaluating Implementations

Execution time

(, )

as performance metric: 3.3 Developing Models
limitations of: 3.3.2 Efficiency and Speedup

Exhaustive search

2.7.1 Floorplan Background

Experimental calibration

3.5.1 Experimental Design, 3.5.1 Experimental Design, 3.5.3 Fitting Data to

Express

Part II: Tools, 8 Message Passing Interface, Chapter Notes, Chapter Notes

Fairness

in CC++: 5.10 Performance Issues
in Fortran M: 6.10 Performance Issues

Fast Fourier transform

4.4 Case Study: Convolution

in convolution: (, )
in convolution: (, )
in HPF: 7.4.2 The INDEPENDENT Directive
performance: Multistage Interconnection Networks.
using hypercube: Chapter Notes

Fine-grained decomposition

2.2 Partitioning

Finite difference algorithm

computation cost: 3.5.3 Fitting Data to
efficiency: 3.3.2 Efficiency and Speedup
execution time: Idle Time.
in CC++: 5.9 Modularity
in Fortran 90: 7.2.2 Array Intrinsic Functions
in Fortran M: 6.9 Modularity
in HPF: 7.3.3 Distribution
in MPI: 8.3.3 Reduction Operations
isoefficiency analysis: 3.4.2 Scalability with Scaled

Finite element method

2.3.3 Unstructured and Dynamic

Fixed problem analysis

3.4.1 Scalability with Fixed

Floorplan optimization problem

description: (, )
description: (, )
parallel algorithms: (, )
parallel algorithms: (, )

Floyd's algorithm

(, ), (, )

Fock matrix problem

algorithms for: Chapter Notes
description: (, )
description: (, )
in CC++: 5.12 Case Study: Fock
in Fortran M: 6.11 Case Study: Fock
in MPI: 8.4 Asynchronous Communication, 8.4 Asynchronous Communication, 8.6.1 Derived Datatypes
in MPI: 8.4 Asynchronous Communication, 8.4 Asynchronous Communication, 8.6.1 Derived Datatypes
in MPI: 8.4 Asynchronous Communication, 8.4 Asynchronous Communication, 8.6.1 Derived Datatypes
performance: 9.4.2 Upshot

Fortran 90

array assignment: 7.2.1 Array Assignment Statement, 7.4 Concurrency
array assignment: 7.2.1 Array Assignment Statement, 7.4 Concurrency
array intrinsics: 7.2.2 Array Intrinsic Functions
as basis for HPF: 7.1.4 Data-Parallel Languages
conformality: 7.1.1 Concurrency, 7.2.1 Array Assignment Statement
conformality: 7.1.1 Concurrency, 7.2.1 Array Assignment Statement
CSHIFT function: 7.2.2 Array Intrinsic Functions
explicit parallelism in: 7.1.1 Concurrency
finite difference problem: 7.2.2 Array Intrinsic Functions
implicit parallelism in: 7.1.1 Concurrency
inquiry functions: 7.6.1 System Inquiry Intrinsic
limitations as data-parallel language: 7.2.2 Array Intrinsic Functions
SIZE function: 7.6.1 System Inquiry Intrinsic
transformational functions: 7.2.2 Array Intrinsic Functions
WHERE: 7.2.1 Array Assignment Statement

Fortran D

Crossbar Switching Network.

Fortran M

Part II: Tools, (, )

and SPMD computations: 6.9 Modularity
argument passing: 6.7 Argument Passing
busy waiting strategy: 6.5 Asynchronous Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
communication: 6.3 Communication
compiler optimization: 6.10 Performance Issues
concurrency: 6.2 Concurrency
concurrency: 6.2 Concurrency
concurrency: 6.2 Concurrency
concurrency: 6.2 Concurrency
concurrency: 6.2 Concurrency
conformality: 6.3.1 Creating Channels
determinism: 6.6 Determinism, 6.7.1 Copying and Determinism
determinism: 6.6 Determinism, 6.7.1 Copying and Determinism
distribution of data: 6.5 Asynchronous Communication
list of extensions: 6.1 FM Introduction
mapping: (, )
mapping: (, )
mapping: (, )
mapping: (, )
mapping: (, )
message passing: 6.9 Modularity, 6.9 Modularity, 6.9 Modularity
message passing: 6.9 Modularity, 6.9 Modularity, 6.9 Modularity
message passing: 6.9 Modularity, 6.9 Modularity, 6.9 Modularity
modularity: 6.1 FM Introduction
modularity: 6.1 FM Introduction
modularity: 6.1 FM Introduction
modularity: 6.1 FM Introduction
performance analysis: 6.10 Performance Issues
port variables: 6.2.1 Defining Processes
process creation: 6.2.2 Creating Processes
quick reference: 6.12 Summary, 6.12 Summary
quick reference: 6.12 Summary, 6.12 Summary
sequential composition: 6.9 Modularity
tree-structured computation: 6.3.3 Receiving Messages

Fujitsu VPP 500

Functional decomposition

(, )

appropriateness: 2.2.2 Functional Decomposition
communication requirements: 2.3 Communication
complement to domain decomposition: 2.2.2 Functional Decomposition
design complexity reduced by: 2.2.2 Functional Decomposition
for climate model: 2.2.2 Functional Decomposition
for Fock matrix problem: Partition.

Functional programming

Chapter Notes, 12 Further Reading

Gantt chart

9.3.2 Traces, 9.4.1 Paragraph, 9.4.2 Upshot

Gauge performance tool

9.4.4 Gauge, Chapter Notes

Gauss-Seidel update

2.3.1 Local Communication, 2.3.1 Local Communication

Gaussian elimination

7.8 Case Study: Gaussian , 9.3.3 Data-Parallel Languages

Genetic sequences

4.5.1 Application

GIGAswitch

Crossbar Switching Network.

Global communication

2.3.2 Global Communication

Grand Challenge problems

Granularity

2.2 Partitioning

agglomeration used to increase: 2.4 Agglomeration
flexibility related to: 2.2 Partitioning
of modular programs: 4.3 Performance Analysis

Handles in MPI

8.2.1 Language Bindings

Hash tables

4.5.2 Implementation

High Performance Fortran

seeHPF

Histograms

9.3.1 Profile and Counts

HPF

Part II: Tools, (, )

abstract processors: 7.3.1 Processors
advantages: 7.9 Summary
collocation of arrays: 7.3.2 Alignment
compilation: 7.7.1 HPF Compilation
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
data distribution: (, )
extrinsic functions: 7.6.3 HPF Features Not
language specification: Chapter Notes
mapping inquiry functions: 7.6.3 HPF Features Not
modularity: 7.5 Dummy Arguments and
modularity: 7.5 Dummy Arguments and
pure functions: 7.6.3 HPF Features Not
remapping of arguments: Strategy 1: Remap
sequence association: 7.6.2 Storage and Sequence
storage association: 7.6.2 Storage and Sequence
subset (official): 7.1.4 Data-Parallel Languages
system inquiry functions: 7.6.1 System Inquiry Intrinsic

Hypercube algorithms

(, )

all-to-all communication: 11 Hypercube Algorithms
matrix transposition: 11.3 Matrix Transposition
parallel mergesort: 11.4 Mergesort
template for: 11 Hypercube Algorithms
vector broadcast: 11.2 Vector Reduction
vector reduction: 11.2 Vector Reduction, 11.2 Vector Reduction
vector reduction: 11.2 Vector Reduction, 11.2 Vector Reduction

Hypercube network

Hypercube Network.

I/O, parallel

applications requiring: 3.8 Input/Output, Chapter Notes
applications requiring: 3.8 Input/Output, Chapter Notes
performance issues: 3.8 Input/Output, 3.8 Input/Output
performance issues: 3.8 Input/Output, 3.8 Input/Output
two-phase strategy: 3.8 Input/Output, Chapter Notes
two-phase strategy: 3.8 Input/Output, Chapter Notes

IBM RP3

IBM SP

Idle Time., 4.3 Performance Analysis

Idle time

Image processing

Exercises, 4.4 Case Study: Convolution

Immersive virtual environments

9.4.3 Pablo

Incremental parallelization

3.2.1 Amdahl's Law

Information hiding

Ensure that modules

Inheritance in C++

5.1.3 Inheritance

Intel DELTA

3.6.2 Speedup Anomalies, Multistage Interconnection Networks., Multistage Interconnection Networks., Chapter Notes

Intel iPSC

1.2.2 Other Machine Models, Chapter Notes, 9.4.5 ParAide

Intel Paragon

Intent declarations

6.7.2 Avoiding Copying

Interconnection Networks

seeNetworks

IPS-2 performance tool

3.4.2 Scalability with Scaled , Chapter Notes

Isoefficiency

J machine

2.3.1 Local Communication

Jacobi update

Journals in parallel computing

Kali

Latency

10.3.2 The Leapfrog Method, 10.3.2 The Leapfrog Method, 10.3.3 Modified Leapfrog

Leapfrog method

Least-squares fit

3.5.3 Fitting Data to , 3.5.3 Fitting Data to

scaled: 3.5.3 Fitting Data to
simple: 3.5.3 Fitting Data to

Linda

and tuple space: 4.5 Case Study: Tuple , Chapter Notes
and tuple space: 4.5 Case Study: Tuple , Chapter Notes
types of parallelism with: Chapter Notes

Load balancing

cyclic methods: Cyclic Mappings.
dynamic methods: 2.5 Mapping
local methods: 2.5 Mapping, Local Algorithms.
local methods: 2.5 Mapping, Local Algorithms.
manager/worker method: Manager/Worker.
probabilistic methods: 2.5 Mapping, Probabilistic Methods.
probabilistic methods: 2.5 Mapping, Probabilistic Methods.
recursive bisection methods: Recursive Bisection.

Local area network

1.3.2 Other Programming Models

Local communication

definition: 2.3.1 Local Communication
finite difference example: (, )
finite difference example: (, )

Locality

and task abstraction: 1.3.1 Tasks and Channels
definition: 1.2.1 The Multicomputer
in CC++: 5.4 Locality
in data-parallel programs: 7.1.2 Locality, 7.8 Case Study: Gaussian
in data-parallel programs: 7.1.2 Locality, 7.8 Case Study: Gaussian
in multicomputers: 1.2.1 The Multicomputer
in PRAM model: 1.2.2 Other Machine Models

Locks

Machine parameters

Mapping

2.1 Methodical Design, (, )

design rules: 2.5.3 Mapping Design Checklist
in CC++: 5.8 Mapping, 5.8.2 Mapping Threads to
in CC++: 5.8 Mapping, 5.8.2 Mapping Threads to
in data-parallel model: 7.1.3 Design
in Fortran M: 6.8 Mapping

Mapping independence

MasPar MP

Matrix multiplication

(, )

1-D decomposition: 4.6.1 Parallel Matrix-Matrix Multiplication
2-D decomposition: 4.6.1 Parallel Matrix-Matrix Multiplication, 4.6.1 Parallel Matrix-Matrix Multiplication
2-D decomposition: 4.6.1 Parallel Matrix-Matrix Multiplication, 4.6.1 Parallel Matrix-Matrix Multiplication
and data distribution neutral libraries: 4.6 Case Study: Matrix
communication cost: 4.6.2 Redistribution Costs
communication structure: 4.6.1 Parallel Matrix-Matrix Multiplication
systolic communication: 4.6.3 A Systolic Algorithm

Matrix transpose

seeTranspose

Meiko CS-2

Member functions in C++

Mentat

Mergesort

(, )

parallel: Compare-Exchange.
parallel algorithms: (, )
parallel algorithms: (, )
performance: Performance
references: Chapter Notes
sequential algorithm: 11.4 Mergesort, 11.4 Mergesort
sequential algorithm: 11.4 Mergesort, 11.4 Mergesort

Mesh networks

Mesh Networks.

Message Passing Interface

seeMPI

Message-passing model

description: Chapter Notes
in HPF: 7.7 Performance Issues
task/channel model comparison: 1.3.2 Other Programming Models

MIMD computers

Modular design

and parallel computing: 1.3 A Parallel Programming , (, )
and parallel computing: 1.3 A Parallel Programming , (, )
and parallel computing: 1.3 A Parallel Programming , (, )
design checklist: Design checklist.
in CC++: 5.9 Modularity
in Fortran M: 6.9 Modularity
in HPF: 7.1.3 Design, 7.5 Dummy Arguments and
in HPF: 7.1.3 Design, 7.5 Dummy Arguments and
in MPI: 8.5 Modularity
in task/channel model: 1.3.1 Tasks and Channels
performance analysis: 4.3 Performance Analysis
principles: (, )
principles: (, )

Monte Carlo methods

MPI

Part II: Tools, (, )

basic functions: 8.2 MPI Basics
C binding: C Language Binding.
collective communication functions: (, )
collective communication functions: (, )
collective communication functions: (, )
collective communication functions: (, )
collective communication functions: (, )
collective communication functions: (, )
collective communication functions: (, )
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
communicators: 8.5 Modularity, 8.5.1 Creating Communicators
derived datatypes: 8.6.1 Derived Datatypes, 8.6.1 Derived Datatypes
derived datatypes: 8.6.1 Derived Datatypes, 8.6.1 Derived Datatypes
determinism: 8.2.2 Determinism, 8.2.2 Determinism
determinism: 8.2.2 Determinism, 8.2.2 Determinism
environmental enquiry: 8.6.2 MPI Features Not
Fortran binding: Fortran Language Binding.
handles: 8.2.1 Language Bindings
message tags: 8.2.2 Determinism
modularity: (, )
modularity: (, )
modularity: (, )
modularity: (, )
MPMD model: 8.1 The MPI Programming
performance issues: 8.7 Performance Issues
probe operations: 8.4 Asynchronous Communication
starting a computation: 8.2 MPI Basics

MPI Forum

MPMD model

8.1 The MPI Programming

MPP Apprentice

1.2.1 The Multicomputer, 3.3 Developing Models

Multicomputer model

and locality: 1.2.1 The Multicomputer
early examples: Chapter Notes

Multicomputer Toolbox

1.2.2 Other Machine Models, 1.2.2 Other Machine Models

Multiprocessors

Multistage networks

Multistage Interconnection Networks., Multistage Interconnection Networks.

nCUBE

1.2.2 Other Machine Models, Chapter Notes, Chapter Notes

NESL

Networks

ATM: 1.2.2 Other Machine Models
bus-based: Bus-based Networks.
crossbar switch: Crossbar Switching Network.
Ethernet: Ethernet.
hypercube: Hypercube Network.
LAN: 1.2.2 Other Machine Models
shared memory: Bus-based Networks.
torus: Mesh Networks.
trends in: 1.1.3 Trends in Networking
WAN: 1.2.2 Other Machine Models

Nondeterminism

from random numbers: 3.5.2 Obtaining and Validating
in Fortran M: 6.6 Determinism
in message-passing model: 8.2.2 Determinism
in MPI: 8.2.2 Determinism
in parameter study problem: 1.4.4 Parameter Study, 1.4.4 Parameter Study
in parameter study problem: 1.4.4 Parameter Study, 1.4.4 Parameter Study

Notation

Terminology

Numerical analysis

Object-oriented model

Objective C

3.6.1 Unaccounted-for Overhead

Out-of-core computation

3.8 Input/Output

Overhead anomalies

Overlapping computation and communication

2.4.2 Preserving Flexibility, Idle Time.

Overloading in C++

5.1.1 Strong Typing and

Owner computes rule

7 High Performance Fortran, 7.1.1 Concurrency, 7.8 Case Study: Gaussian

P++ library

Part II: Tools, 8 Message Passing Interface, Chapter Notes

p4

Pablo performance tool

9.4.3 Pablo, Chapter Notes

Pairwise interactions

(, )

in Fortran M: 6.3.3 Receiving Messages
in HPF: 7.3.3 Distribution
in MPI: Fortran Language Binding., Fortran Language Binding., 8.2.2 Determinism
in MPI: Fortran Language Binding., Fortran Language Binding., 8.2.2 Determinism
in MPI: Fortran Language Binding., Fortran Language Binding., 8.2.2 Determinism

Paragraph performance tool

9.4.1 Paragraph, Chapter Notes

ParAide performance tool

9.4.5 ParAide, Chapter Notes

Parallel algorithm design

bibliography: 12 Further Reading
and performance: 3.10 Summary
case studies: (, )
case studies: (, )
methodology: 2.1 Methodical Design, 2.9 Summary
methodology: 2.1 Methodical Design, 2.9 Summary

Parallel algorithms

branch and bound search: 2.7.1 Floorplan Background
convolution: 4.4 Case Study: Convolution
fast Fourier transform: 4.4 Case Study: Convolution
Gaussian elimination: 7.8 Case Study: Gaussian , 9.3.3 Data-Parallel Languages
Gaussian elimination: 7.8 Case Study: Gaussian , 9.3.3 Data-Parallel Languages
matrix multiplication: (, )
matrix multiplication: (, )
mergesort: 11.4 Mergesort
parallel prefix: 7.6.3 HPF Features Not
parallel suffix: 7.6.3 HPF Features Not
quicksort: Chapter Notes
random number generation: 10 Random Numbers
reduction: 2.3.2 Global Communication
search: Chapter Notes
shortest paths: 3.9 Case Study: Shortest-Path
spectral transform: Multistage Interconnection Networks.
transpose: Exercises, 11.3 Matrix Transposition
transpose: Exercises, 11.3 Matrix Transposition
vector reduction: 11.1 The Hypercube Template

Parallel composition

4.2 Modularity and Parallel , (, )

in CC++: 5.9 Modularity
in convolution algorithm: 4.4.2 Composing Components
in Fortran M: 6.1 FM Introduction
in MPI: 8.5 Modularity
load imbalances due to: 4.3 Performance Analysis
task parallel approach: 8.5.2 Partitioning Processes
vs. SPMD model: 4.2.3 Parallel Composition

Parallel computers

applications: 1.1.1 Trends in Applications
architecture: (, ), (, )
architecture: (, ), (, )
architecture: (, ), (, )
architecture: (, ), (, )
definition: 1.1 Parallelism and Computing
performance trends: 1.1.2 Trends in Computer

Parallel computing conferences

Parallel computing journals

Parallel database machines

1.4.4 Parameter Study, 1.4.4 Parameter Study, 1.4.4 Parameter Study, 1.4.4 Parameter Study

Parallel I/O

seeI/O, parallel

Parallel prefix

7.6.3 HPF Features Not

Parallel programming models

message passing: 1.3.2 Other Programming Models
data parallelism: 1.3.2 Other Programming Models
MPMD: 8.1 The MPI Programming
shared memory: 1.3.2 Other Programming Models
SPMD: 1.3.2 Other Programming Models
survey: Chapter Notes
task/channel: 1.3.1 Tasks and Channels

Parallel software requirements

concurrency: 1.1.2 Trends in Computer , 1.1.4 Summary of Trends
concurrency: 1.1.2 Trends in Computer , 1.1.4 Summary of Trends
locality: 1.2.1 The Multicomputer
modularity: 1.3 A Parallel Programming
scalability: 1.1.4 Summary of Trends

Parallel suffix

7.6.3 HPF Features Not

Parallelism trends

in applications: 1.1.1 Trends in Applications
in computer design: 1.1.2 Trends in Computer

Parameter study problem

PARMACS

Part II: Tools, 8 Message Passing Interface, Chapter Notes

Partitioning

and domain decomposition: 2.2 Partitioning
and functional decomposition: 2.2 Partitioning
design checklist: 2.2.3 Partitioning Design Checklist

Partitioning algorithms

2.5.1 Load-Balancing Algorithms

pC++

Chapter Notes, 7 High Performance Fortran, 7.1.1 Concurrency, Chapter Notes

PCAM

2.1 Methodical Design, 2.1 Methodical Design, 7.1.3 Design

Per-hop time

Per-word transfer time

Performance modeling

Amdahl's law: 3.2.1 Amdahl's Law, 3.2.1 Amdahl's Law
Amdahl's law: 3.2.1 Amdahl's Law, 3.2.1 Amdahl's Law
asymptotic analysis: 3.2.3 Asymptotic Analysis
design considerations: 3.4 Scalability Analysis
empirical studies: 3.2.2 Extrapolation from Observations, 3.4 Scalability Analysis, 3.5 Experimental Studies
empirical studies: 3.2.2 Extrapolation from Observations, 3.4 Scalability Analysis, 3.5 Experimental Studies
empirical studies: 3.2.2 Extrapolation from Observations, 3.4 Scalability Analysis, 3.5 Experimental Studies
for evaluation of algorithm implementation: 3.6 Evaluating Implementations
for I/O: 3.8 Input/Output
impact of interconnection networks: 3.7.2 Interconnection Networks
methodology: Chapter Notes, 9.1 Performance Analysis, 9.2.2 Counters
methodology: Chapter Notes, 9.1 Performance Analysis, 9.2.2 Counters
methodology: Chapter Notes, 9.1 Performance Analysis, 9.2.2 Counters
metrics: 3.3 Developing Models, 3.3.2 Efficiency and Speedup
metrics: 3.3 Developing Models, 3.3.2 Efficiency and Speedup
qualitative analysis: 3.4 Scalability Analysis
with multiple modules: 4.3 Performance Analysis

Performance tools

seeTools, performance

Performance trends

in networking: 1.1.3 Trends in Networking
in parallel computers: 1.1.1 Trends in Applications

Performance, definition

Performance, metrics

PETSc

PICL

Avoiding Communication., 4.4.1 Components

Pipelining

Poison pill technique

4.5.1 Application

Polling

2.3.4 Asynchronous Communication

costs: 2.3.4 Asynchronous Communication
for load balancing: Decentralized Schemes.
in CC++: 5.6 Asynchronous Communication
in Fortran M: 6.5 Asynchronous Communication
in MPI: (, )
in MPI: (, )

Ports

1.3.1 Tasks and Channels, 6.2.1 Defining Processes

PRAM model

1.2.2 Other Machine Models, Chapter Notes, Chapter Notes, 3.2.3 Asymptotic Analysis

Prefetching

1.4.4 Parameter Study, Manager/Worker.

Prefix product

Communication.

Prism performance tool

9.3.3 Data-Parallel Languages, Chapter Notes

Probabilistic methods for load balancing

Probabilistic Methods.

Probe effect

9.2.3 Traces

Processes in MPI

8.1 The MPI Programming

Production systems

9.1 Performance Analysis, 9.2.1 Profiles

Profiles

advantages: 9.1 Performance Analysis, 9.2.1 Profiles
advantages: 9.1 Performance Analysis, 9.2.1 Profiles
data reduction techniques: 9.3.1 Profile and Counts
disadvantages: 9.2.1 Profiles
sampling approach: Chapter Notes

Protection in C++

2.7.1 Floorplan Background, 2.7.1 Floorplan Background

Pruning

Pseudo-random numbers

seeRandom numbers

PVM

Part II: Tools, 8 Message Passing Interface, Chapter Notes

Quick references

for CC++: 5.13 Summary, 5.13 Summary
for CC++: 5.13 Summary, 5.13 Summary
for Fortran M: 6.12 Summary, 6.12 Summary
for Fortran M: 6.12 Summary, 6.12 Summary

Quicksort

10.3.1 The Random Tree , 10.3.1 The Random Tree

Random numbers

centralized generators: 10.2 Parallel Random Numbers
distributed generators: 10.2 Parallel Random Numbers
leapfrog method: 10.3.2 The Leapfrog Method
linear congruential generators: 10.1 Sequential Random Numbers, 10.3.1 The Random Tree
linear congruential generators: 10.1 Sequential Random Numbers, 10.3.1 The Random Tree
modified leapfrog method: 10.3.3 Modified Leapfrog
parallel: 10 Random Numbers, 10.2 Parallel Random Numbers
parallel: 10 Random Numbers, 10.2 Parallel Random Numbers
period of the generator: 10.1 Sequential Random Numbers
random tree method: 10.3 Distributed Random Generators, 10.3.1 The Random Tree
random tree method: 10.3 Distributed Random Generators, 10.3.1 The Random Tree
replicated generators: 10.2 Parallel Random Numbers
sequential: 10 Random Numbers, 10.1 Sequential Random Numbers
sequential: 10 Random Numbers, 10.1 Sequential Random Numbers
tests for generators: Chapter Notes
use with Monte Carlo methods: Chapter Notes

Random tree method

Real-time applications

Receiver-initiated strategy

11.2 Vector Reduction, 11.2 Vector Reduction

Recursive bisection

(, )

coordinate: Recursive Bisection., Chapter Notes
coordinate: Recursive Bisection., Chapter Notes
graph: Recursive Bisection.
spectral: Recursive Bisection.
unbalanced: Recursive Bisection.

Recursive halving algorithm

Red-black algorithm

2.3.1 Local Communication

Reduction

2.3.2 Global Communication

in Fortran 90: 7.2.2 Array Intrinsic Functions
in MPI: 8.3 Global Operations, 8.3.3 Reduction Operations
in MPI: 8.3 Global Operations, 8.3.3 Reduction Operations

Remote procedure call

5.12 Case Study: Fock

Replication

of computation: Replicating Computation., Replicating Computation.
of computation: Replicating Computation., Replicating Computation.
of data: Communication and Agglomeration., Communication and Agglomeration.
of data: Communication and Agglomeration., Communication and Agglomeration.

Ring pipeline

seePairwise interactions

RPC

5.12 Case Study: Fock

Scalability

4.2.2 Sequential Composition, 4.2.2 Sequential Composition, Chapter Notes

Scalability analysis

(, )

ScaLAPACK

Scale analysis

3.3 Developing Models

Scaled speedup

Search

Self Describing Data Format

9.2.3 Traces, 9.4.3 Pablo

Self-consistent field method

1.3.2 Other Programming Models

Semaphores

Sender-initiated strategy

7.6.2 Storage and Sequence

Sequence association

Sequent Symmetry

7.7.2 Sequential Bottlenecks

Sequential bottlenecks in HPF

Sequential composition

4.2 Modularity and Parallel , (, )

advantages: 4.2.2 Sequential Composition, 4.2.2 Sequential Composition
advantages: 4.2.2 Sequential Composition, 4.2.2 Sequential Composition
and parallel libraries: 4.2.2 Sequential Composition
convolution example: 4.4.2 Composing Components
example: 4.2.2 Sequential Composition
in CC++: 5.9 Modularity, 5.9 Modularity, 5.9 Modularity
in CC++: 5.9 Modularity, 5.9 Modularity, 5.9 Modularity
in CC++: 5.9 Modularity, 5.9 Modularity, 5.9 Modularity
in Fortran M: 6.9 Modularity, 6.9 Modularity
in Fortran M: 6.9 Modularity, 6.9 Modularity
in HPF: 7.5 Dummy Arguments and
in MPI: 8.5 Modularity

Sets, distributed

4.5 Case Study: Tuple

Shared-memory model

1.3.2 Other Programming Models, Chapter Notes, Bus-based Networks.

Shortest-path problem

(, )

algorithm comparison: 3.9.3 Shortest-Path Algorithms Summary
all-pairs: 3.9.1 Floyd's Algorithm
Dijkstra's algorithm: 3.9.2 Dijkstra's Algorithm
Floyd's algorithm: 3.9.1 Floyd's Algorithm
requirements: 3.9 Case Study: Shortest-Path
single-source: 3.9 Case Study: Shortest-Path , 3.9.2 Dijkstra's Algorithm
single-source: 3.9 Case Study: Shortest-Path , 3.9.2 Dijkstra's Algorithm

Silicon Graphics Challenge

1.2.2 Other Machine Models, 1.2.2 Other Machine Models, Chapter Notes

SIMD computer

Single program multiple data

seeSPMD model

Single-assignment variable

SISAL

11.4 Mergesort, Chapter Notes

Sorting

Space-time diagrams

9.3.2 Traces

Sparse matrix algorithms

Recursive Bisection., Chapter Notes

Spectral bisection

Spectral transform

Multistage Interconnection Networks.

Speed of light

3.7.1 Competition for Bandwidth

Speedup

absolute: 3.3.2 Efficiency and Speedup
anomalies: 3.6.2 Speedup Anomalies, Chapter Notes
anomalies: 3.6.2 Speedup Anomalies, Chapter Notes
relative: 3.3.2 Efficiency and Speedup
superlinear: 3.6.2 Speedup Anomalies, Chapter Notes
superlinear: 3.6.2 Speedup Anomalies, Chapter Notes

SPMD model

1.4 Parallel Algorithm Examples

agglomeration phase: 2.4 Agglomeration
and parallel composition: 4.2.3 Parallel Composition
and PCAM methodology: 2.1 Methodical Design
and sequential composition: 4.2.2 Sequential Composition
in CC++: 5.8.2 Mapping Threads to
in Fortran M: 6.9 Modularity
in HPF: 7.1.1 Concurrency
in MPI: 8.1 The MPI Programming
limitations: 1.3.2 Other Programming Models

Startup time

2.3.1 Local Communication

Stencil of grid point

Storage association

7.6.2 Storage and Sequence

Superlinear speedup

3.6.2 Speedup Anomalies, 3.6.2 Speedup Anomalies

arguments against: Chapter Notes

Surface-to-volume effect

Surface-to-Volume Effects., Surface-to-Volume Effects., 3.4.2 Scalability with Scaled

Synchronization

1.4.1 Finite Differences, 5.5.2 Synchronization, 8.3.1 Barrier, 11.2 Vector Reduction

Systolic communication

4.6.3 A Systolic Algorithm

t

seestartup time

t

seeper-word transfer time

t

seeper-hop time

Task parallelism

8.5.2 Partitioning Processes

Task scheduling

decentralized control: Decentralized Schemes.
for floorplan optimization: Mapping.
for short-lived tasks: 2.5 Mapping
hierarchical: Hierarchical Manager/Worker.
manager/worker: Manager/Worker.
problem allocation: 2.5.2 Task-Scheduling Algorithms
termination detection: Termination Detection.
with task pool: 2.5.2 Task-Scheduling Algorithms

Task/channel model

(, )

data-parallel model comparison: 7.1.3 Design
description: Chapter Notes
determinism: 1.3.1 Tasks and Channels
locality: 1.3.1 Tasks and Channels
mapping: 1.3.1 Tasks and Channels, 1.3.1 Tasks and Channels
mapping: 1.3.1 Tasks and Channels, 1.3.1 Tasks and Channels
message-passing model comparison: 1.3.2 Other Programming Models
modularity: 1.3.1 Tasks and Channels
object-oriented model comparison: 1.3.1 Tasks and Channels
performance: 1.3.1 Tasks and Channels
scalability: 1.3.1 Tasks and Channels

Template

definition: 11 Hypercube Algorithms
for hypercube: 11 Hypercube Algorithms
in HPF: 7.6.3 HPF Features Not

Termination detection

Decentralized Schemes., Chapter Notes

Terminology

Terminology

Thinking Machines CM2

Thinking Machines CM5

Threads in CC++

5.2 CC++ Introduction

Throughput

3.5.2 Obtaining and Validating

Timers

9.2.2 Counters

Timing variations

Tools, performance

AIMS: Chapter Notes
customized: 9.4.8 Custom Tools
Gauge: 9.4.4 Gauge
IPS-2: Chapter Notes
MPP Apprentice: Chapter Notes
Pablo: 9.4.3 Pablo
Paragraph: 9.4.1 Paragraph
ParAide: Chapter Notes
Prism: 9.3.3 Data-Parallel Languages, Chapter Notes
Prism: 9.3.3 Data-Parallel Languages, Chapter Notes
selection of: 9.1 Performance Analysis
standards lacking for: 9 Performance Tools
Upshot: 9.4.2 Upshot
VT: 9.4.6 IBM's Parallel Environment

Torus networks

Mesh Networks.

Traces

(, ), 9.3.2 Traces

disadvantages: 9.2.3 Traces, 9.2.3 Traces
disadvantages: 9.2.3 Traces, 9.2.3 Traces
standards lacking for: 9.2.3 Traces

Transformation of data

9.1 Performance Analysis

Transpose

4.5 Case Study: Tuple , 4.5.2 Implementation

hypercube algorithm: 11.3 Matrix Transposition
in convolution: 4.4.1 Components

Tree search

1.4.3 Search

in CC++: 5.4.3 Thread Placement
in Fortran M: 6.3.3 Receiving Messages

Trends

in applications: 1.1.1 Trends in Applications
in computer design: 1.1.2 Trends in Computer

Tuple space

Ultracomputer

Unbalanced recursive bisection

Recursive Bisection.

Unity

2.3.3 Unstructured and Dynamic , 2.3.3 Unstructured and Dynamic

Unstructured communication

Upshot performance tool

state data analysis: 9.4.2 Upshot
use with MPI: 9.4.2 Upshot

Vector broadcast algorithm

11.2 Vector Reduction

Vector reduction

11.2 Vector Reduction

Video servers

Vienna Fortran

1.1.2 Trends in Computer , 2.7.1 Floorplan Background, Chapter Notes, 12 Further Reading

Virtual computers

6.8.1 Virtual Computers

Virtual functions in C++

5.1.3 Inheritance

Visualization of performance data

9.3.2 Traces, Chapter Notes

VLSI design

Von Neumann computer

derivation: Chapter Notes
exposition on: Chapter Notes
illustration: 1.2 A Parallel Machine
model: 1.2 A Parallel Machine
program structure: 1.3 A Parallel Programming

VT performance tool

9.4.6 IBM's Parallel Environment

Wide area network

Zipcode