A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand
Ziedan, I., Shehata, H., Seraga, S. (2016). A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand. EKB Journal Management System, 20(EIJEST, Vol. 20, 2016), 1-9. doi: 10.21608/eijest.2016.97168
I Ziedan; H Shehata; S Seraga. "A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand". EKB Journal Management System, 20, EIJEST, Vol. 20, 2016, 2016, 1-9. doi: 10.21608/eijest.2016.97168
Ziedan, I., Shehata, H., Seraga, S. (2016). 'A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand', EKB Journal Management System, 20(EIJEST, Vol. 20, 2016), pp. 1-9. doi: 10.21608/eijest.2016.97168
Ziedan, I., Shehata, H., Seraga, S. A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand. EKB Journal Management System, 2016; 20(EIJEST, Vol. 20, 2016): 1-9. doi: 10.21608/eijest.2016.97168

A Run-Time Program Phase Detection Technique for Optimizing Per-Phase L2 Cache Demand

The Egyptian International Journal of Engineering Sciences and Technology
Article 1, Volume 20, EIJEST, Vol. 20, 2016, September 2016, Page 1-9  XML PDF (415.59 K)
Document Type: Original Article
DOI: 10.21608/eijest.2016.97168
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Authors
I Ziedan* 1; H Shehata2; S Seraga2
1Computers and Systems Department, Zagazig University, Zagazig 44519, Egypt.
2Computers and Systems Department, Zagazig University, Zagazig 44519, Egypt
Abstract
Understanding program behavior is at the foundation of computer architecture and
program optimization. Programs pass through different behaviors where their
performance characteristics and hardware resource requirements vary. Program
phase detection and classification research aiming to understand the program timevarying
behavior, can unlock a lot of phase-based optimizations which are specially
tailored to improve the performance of each individual program phase. In this paper,
we introduce an efficient run-time phase detection and classification technique, based
on tracking changes in the L2 cache access pattern of different portions in the
program execution. The proposed technique monitors a running program and keeps
track what phase the running program is currently executing, with no need to
recompile the tracked program, and with execution time overhead of 4%, on average.
Performance Monitoring Unit (PMU) is exploited to sample the memory addresses
causing L1 data cache misses. This profiling data is used to construct the Cache
Access Signature Vectors (CASVs) that accurately reflect the L2 cache access
patterns for each interval of execution. By comparing CASVs, the proposed
technique classifies the program into a set of stable phases with high degree of intraphase
homogeneity. Our evaluation shows that phase changes detected by our
technique have strong correlation with the variation in Instruction Per Cycle (IPC).
Furthermore, our technique can contribute in reducing L2 cache miss rates, and
optimizing L2 cache utilization, through its direct capability of estimating per-phase
L2 cache demand
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