CSC/ECE 506 Fall 2007/wiki1 12 dp3: Difference between revisions
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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. | 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 : | 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 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 | 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 | ||
=== Artifacts of Measuring Performance === | === Artifacts of Measuring Performance === | ||
Revision as of 21:29, 2 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
- What is this?
- Why is this important to measure?
- How this is measured?
In this introduction section, we describe why performance issue is important for parallel computer architects and what are the metrices of performance.
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 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
Artifacts of Measuring Performance
Data Transfer
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!