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CMPEN 431 Design Space Exploration

创建日期：2024-06-21 17:22:38

所属分类：计算机computer science

标签： CMPEN 431

Design Space Exploration

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CMPEN 431

Design Space Exploration
Solutions to project assignments are to be developed individually, without collaboration. However, as the projects in this class require the use of software tools and frameworks that students may have uneven prior familiarity with, discussion and assistance
among students in gaining expertise with these software tools constitutes acceptable behavior. Note that this assistance and discussion cannot include the sharing of access
to any code produced in solution to the project assignments. In order to avoid potential ambiguity in what constitutes ”code produced in solution to the project assignment,”
students wishing to aid their peers with auxiliary supporting scripts, mechanisms, or examples are directed to pass any such artifacts to the course staff for vetting and possible
inclusion on project-specific FAQs rather than share it with their peers directly.
In this project, you are going to use SimpleScalar as the evaluation engine to perform
a design space exploration, using the provided framework code, over a 18-dimensional
processor pipeline and memory hierarchy design space (some of these dimensions are not
independent). You will use a 5-benchmark suite as the workload.
You should use CSE Linux Lab machines (i.e. cse-p204instXX.cse.psu.edu which XX
is a 2-digit number from 01 to 40). We will test your results on those machines.
1. Project Goal
Your assignment is to, with an evaluation count limit of 1000 design points, explore the
design space in order to select the best performing design under a set of two different
optimization functions. These include:
1. The “best” performing overall design (in term of the geometric mean of normalized execution time normalized across all benchmarks)
2. The most energy-efficient design (as measured by the lowest geometric mean of
normalized energy-delay product [units of energy delay product are joule-seconds]
across all benchmarks)
2. Background
2.1. SimpleScalar
SimpleScalar is an architectural simulator which enables a study of how different processor and memory system parameters affect performance and energy efficiency. The
simulator accepts a set of system design parameters and an executable (workload) to run
on the described system. A wide range of system statistics are recorded by the simulator
as the executable runs on the simulated system. Once the framework in this project is
setup, interested readers can have a look at one of the log files in rawProjectOutputData
folder to view SimpleScalar output.
This project heavily uses SimpleScalar but most of the interface is abstracted out by a
simpler framework interface. Nevertheless, you can refer to this SimpleScalar guide for
details about parameters passed to SimpleScalar.
2.2. Design Space Exploration
Given a set of design parameters, Design Space Exploration (DSE) involves probing various design points to find the most suitable design to meet required goals. Follow this
quick reading about DSE before moving ahead.
DSE can be performed for different design goals. For example, one DSE may want to
find the best performing design whereas another DSE may be aimed at finding the most
energy efficient design. A more complex DSE may look for the best performing design
given a fixed energy budget.
An exhaustive DSE simply tries out all possible combinations of parameter values to
find the absolute best design. However, as the size of design space increases this approach
quickly becomes infeasible. Consider a 10-dimensional design space with 5 possible
values for each parameter and 2 minutes simulation time to evaluate a given design point;
an exhaustive search will take 5
10 ∗ 2min ≈ 37years.
A more intelligent DSE employs heuristics to intelligently prune down the design space
and to prioritize evaluation of more reasonable design points first. If the assumptions
employed by the heuristics are correct, the DSE will still result in the best design. On the
other hand. with a set of reasonably justified assumptions a heuristic can result in a “good
enough” design point.
2.3. Energy-Delay Product
Energy-Delay Product (EDP) is a metric which consolidates both performance and energy
efficiency.
EDP = total execution energy * execution time
Design A takes 100pJ to process an image in 100ms, EDP = 10000 units. Design B
takes 80pJ to process an image in 2000ms, EDP = 160000. Design A is clearly more
energy efficient, but it performs poorly as it incurs more execution time. EDP enables a
more holistic design comparison.

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