Measuring computer performance: Preface

480 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Temperature Effects on Productivity and Factor Reallocation: Evidence from a Half Million Chinese Manufacturing Plants (2018, background): “For example, higher temperatures lower the ability of lubricants to reduce surface friction between mechanical components (Mortier et al., 1992); increase failure rates by expanding the volume of input materials (Collins, 1963); and lower the processing speed of computers (Lilja, 2000).”
MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research (2002, methodology): “We compared the MinneSPEC function-level execution profiles and instruction mix profiles to the profiles produced with the SPEC reference inputs using the chi-squared goodness-of-fit test [12].”
Statistically rigorous java performance evaluation (2007, methodology): “This section describes fundamental statistics theory as described in many statistics textbooks, see for example [15, 16, 19], and discusses how statistics theory applies to Java performance data analysis.”
Scientiﬁc Benchmarking of Parallel Computing Systems Twelve ways to tell the masses when reporting performance results (2017, background): “Our experiments (Figure 2) and other authors [12] show that the 30-40 samples as indicated in some textbooks [38] are not su ﬃ - cient.”