Journal
50TH ANNUAL IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE (MICRO)
Volume -, Issue -, Pages 651-664Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3123939.3124541
Keywords
Uncertainty; Random Variable; Core Selection; Architecture Modelling
Funding
- National Science Foundation [1239567, 1162187, 1563935]
- Direct For Computer & Info Scie & Enginr [1162187, 1239567, 1563935] Funding Source: National Science Foundation
- Division Of Computer and Network Systems [1162187, 1239567, 1563935] Funding Source: National Science Foundation
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Designing a system in an era of rapidly evolving application behaviors and significant technology shifts involves taking on risk that a design will fail to meet its performance goals. While risk assessment and management are expected in both business and investment, these aspects are typically treated as independent to questions of performance and efficiency in architecture analysis. As hardware and software characteristics become uncertain (i.e. samples from a distribution), we demonstrate that the resulting performance distributions quickly grow beyond our ability to reason about with intuition alone. We further show that knowledge of the performance distribution can be used to significantly improve both the average case performance and minimize the risk of under-performance (which we term architectural risk). Our automated framework can be used to quantify the areas where trade-offs between expected performance and the tail of performance are most acute and provide new insights supporting architectural decision making (such as core selection) under uncertainty. Importantly it can do this even without a priori knowledge of an analytic model governing that uncertainty.
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