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CMPEN 431 Design Space Exploration

Creation date：2024-06-21 17:22:38

Belonging category：计算机computer science

Tag 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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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum

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