CSC/ECE 506 Fall 2007/wiki1 12 dp3: Difference between revisions

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=== Introduction ===
=== Introduction ===


::: What is this?
In this introduction section, we describe why performance issue is important for parallel computer architects and what the metrics of performance are.  
::: Why is this important to measure?
As we already know performance is a very important issue in uniprocessor system where architects always try to improve performance of system in terms of execution time by using several techniques such as minimizing memory access time (to access the data fast), designing hardware which can execute many instruction in parallel and possibly faster ( micro level parallelism extraction).Consequently, performance issue plays a big role in parallel computing for several reasons like data is shared among many processors and hence processes on different processors need to communicate efficiently and coherently.  
::: How this is measured?
To make our point more precise, let us consider the following example:
 
Assume we want to run a program which takes 100ms on uniprocessor with no pipeline scheme. However, we also know that the full program can be decomposed in many processes and these processes can be run on different machine. So basically, in best case we get the speed up of ‘n’ where is the number of processors available. We might have virtually divided the computing load but these processes are not independent so in order to complete the program they do need to communicate to each other for data sharing, synchronization etc. So there is communication overhead involved in parallel computing.  A wise architect would not like to have any parallel system where communication overhead overwhelms speed up achieved by dividing the work load. So we need to analyze the performance considering various trade-offs.
In this introduction section, we describe why performance issue is important for parallel computer architects and what are the metrices of performance.
There are three basic performance metrics.
 
1. Latency : Time taken by a operation to get completed.(seconds per operation)
As we already know performance is a very important issue in uniprocessor system where architects always try to improve performance of system in terms of execution time by using several techniques such as minimizing memory access time (to access the data fast), designing hardware which can execute many instruction in parallel and possibly faster ( micro level parallelism extraction).Consequently, performance issue plays a big role in parallel computing for several reasons like data is shared among many processors and hence processes on different processors need to communicate efficiently and coherently.
2. Bandwidth: The rate at which operations are performed ( operations per second)
To make our point more precise, let us consider the following example :
3. Cost: It is the measure of impact operations have on total execution time of the program. (latency times number of operations)
Assume we want to run a program which takes 100ms on uniprocessor with no pipeline scheme. However, we also know that the full program can be decomposed in many tasks and again tasks can be grouped to form many processes and these processes can be run on many processors (not necesaarily one-to-one mapping).Suppose we have three processors which can support at least one parallel programming model and we end up hafour processes
In parallel computing many operations are performed concurrently so relationship among performance metrics is not simple and we need to consider the performance for communication operations in parallel computing and see how they are measured using these three matrices.
As data transfer operations are the most frequent type of communication operation, we will first discuss that.


=== Artifacts of Measuring Performance ===
=== Artifacts of Measuring Performance ===

Revision as of 01:11, 6 September 2007

Sections 1.3.3 and 1.3.4: Most changes here are probably related to performance metrics. Cite other models for measuring artifacts such as data-transfer time, overhead, occupancy, and communication cost. Focus on the models that are most useful in practice.

Communication and Replication

Performance

Introduction

In this introduction section, we describe why performance issue is important for parallel computer architects and what the metrics of performance are. As we already know performance is a very important issue in uniprocessor system where architects always try to improve performance of system in terms of execution time by using several techniques such as minimizing memory access time (to access the data fast), designing hardware which can execute many instruction in parallel and possibly faster ( micro level parallelism extraction).Consequently, performance issue plays a big role in parallel computing for several reasons like data is shared among many processors and hence processes on different processors need to communicate efficiently and coherently. To make our point more precise, let us consider the following example:

Assume we want to run a program which takes 100ms on uniprocessor with no pipeline scheme. However, we also know that the full program can be decomposed in many processes and these processes can be run on different machine. So basically, in best case we get the speed up of ‘n’ where is the number of processors available. We might have virtually divided the computing load but these processes are not independent so in order to complete the program they do need to communicate to each other for data sharing, synchronization etc. So there is communication overhead involved in parallel computing.  A wise architect would not like to have any parallel system where communication overhead overwhelms speed up achieved by dividing the work load. So we need to analyze the performance considering various trade-offs.

There are three basic performance metrics. 1. Latency : Time taken by a operation to get completed.(seconds per operation) 2. Bandwidth: The rate at which operations are performed ( operations per second) 3. Cost: It is the measure of impact operations have on total execution time of the program. (latency times number of operations) In parallel computing many operations are performed concurrently so relationship among performance metrics is not simple and we need to consider the performance for communication operations in parallel computing and see how they are measured using these three matrices. As data transfer operations are the most frequent type of communication operation, we will first discuss that.

Artifacts of Measuring Performance

Data Transfer

How to measure- Mathematical Equation[1]
Quantitative Measure
Implication of such measurement
Advantage and Disadvantage[2]
New Advances


Overhead and Occupancy

Communication Cost

Linking to previous Artifacts.
How to measure- Mathematical Equation
Quantitative Measure
Implication of such measurement
Advantage and Disadvantage
New Advances

New Artifact

not decided yet!