A Metric-Guided Method for Discovering Impactful Features and Architectural Insights for Skylake-Based Processors

Author:

Yasin Ahmad1ORCID,Haj-Yahya Jawad2ORCID,Ben-Asher Yosi3,Mendelson Avi4

Affiliation:

1. University of Haifa and Intel Corporation

2. ETH Zurich, Switzerland

3. University of Haifa, Mount Carmel, Haifa, Israel

4. Technion, Technion City, Haifa, Israel

Abstract

The slowdown in technology scaling puts architectural features at the forefront of the innovation in modern processors. This article presents a Metric-Guided Method (MGM) that extends Top-Down analysis with carefully selected, dynamically adapted metrics in a structured approach. Using MGM, we conduct two evaluations at the microarchitecture and the Instruction Set Architecture (ISA) levels. Our results show that simple optimizations, such as improved representation of CISC instructions, broadly improve performance, while changes in the Floating-Point execution units had mixed impact. Overall, we report 10 architectural insights—at the microarchitecture, ISA, and compiler fronts—while quantifying their impact on the SPEC CPU benchmarks.

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Information Systems,Software

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Performance Analysis of the NVIDIA HPC SDK and AMD AOCC Compilers in an HPC Cluster Using Pooled, Robust and Relative Metrics;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

2. Efficient Cross-platform Multiplexing of Hardware Performance Counters via Adaptive Grouping;ACM Transactions on Architecture and Code Optimization;2024-01-19

3. From Top-down Microarchitecture Analysis to Structured Performance Optimizations;2023-09-11

4. Flexible system software scheduling for asymmetric multicore systems with PMCSched: A case for Intel Alder Lake;Concurrency and Computation: Practice and Experience;2023-06-06

5. Performance Analysis with Unified Hardware Counter Metrics;2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS);2022-11

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