Computer Hardware

Donated on 9/30/1987

Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.

Dataset Characteristics

Multivariate

Subject Area

Computer Science

Associated Tasks

Regression

Feature Type

Integer

# Instances

209

# Features

10

Dataset Information

Additional Information

The estimated relative performance values were estimated by the authors using a linear regression method. See their article (pp 308-313) for more details on how the relative performance values were set.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
VendorNameFeatureCategorical(adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, sratus, wang)no
ModelNameFeatureCategoricalmany unique symbolsno
MYCTFeatureIntegermachine cycle timenanosecondsno
MMINFeatureIntegerminimum main memorykilobytesno
MMAXFeatureIntegermaximum main memorykilobytesno
CACHFeatureIntegercache memorykilobytesno
CHMINFeatureIntegerminimum channelsunitsno
CHMAXFeatureIntegermaximum channelsunitsno
PRPFeatureIntegerpublished relative performanceno
ERPFeatureIntegerestimated relative performance from the original articleno

0 to 10 of 10

Additional Variable Information

1. vendor name: 30 (adviser, amdahl,apollo, basf, bti, burroughs, c.r.d, cambex, cdc, dec, dg, formation, four-phase, gould, honeywell, hp, ibm, ipl, magnuson, microdata, nas, ncr, nixdorf, perkin-elmer, prime, siemens, sperry, sratus, wang) 2. Model Name: many unique symbols 3. MYCT: machine cycle time in nanoseconds (integer) 4. MMIN: minimum main memory in kilobytes (integer) 5. MMAX: maximum main memory in kilobytes (integer) 6. CACH: cache memory in kilobytes (integer) 7. CHMIN: minimum channels in units (integer) 8. CHMAX: maximum channels in units (integer) 9. PRP: published relative performance (integer) 10. ERP: estimated relative performance from the original article (integer)

Papers Citing this Dataset

A greedy constructive algorithm for the optimization of neural network architectures

By Massimiliano Pasini, Junqi Yin, Ying Li, Markus Eisenbach. 2019

Published in ArXiv.

Universum Learning for SVM Regression

By Sauptik Dhar, Vladimir Cherkassky. 2016

Published in ArXiv.

0 to 2 of 2

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2 citations
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Creators

Jacob Feldmesser

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