All Research Papers Published In Performance Evaluation

Front Matter
10.1016/j.peva.2024.102430

Preface: Special issue on ACM/SPEC ICPE 2023

Jul 25, 2024
Performance Evaluation
Antinisca Di Marco + 1 more +1

Research Article
6
10.1016/j.peva.2024.102429

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

Jul 14, 2024
Performance Evaluation
Zhuojin Li + 2 more +2

Research Article
1
10.1016/j.peva.2024.102409

A dependence graph pattern mining method for processor performance analysis

Feb 28, 2024
Performance Evaluation
Yawen Zheng + 4 more +4

Research Article
1
10.1016/j.peva.2024.102408

The impact of load comparison errors on the power-of-[formula omitted] load balancing

Feb 28, 2024
Performance Evaluation
Sanidhay Bhambay + 2 more +2

Research Article
2
10.1016/j.peva.2024.102397

On the sensitivity of stationary solutions of Markov regenerative processes

Jan 29, 2024
Performance Evaluation
Junjun Zheng + 2 more +2

Research Article
1
10.1016/j.peva.2023.102383

Marginal and joint distribution of inter-departure times for a PH/PH/c queue

Nov 04, 2023
Performance Evaluation
Ruth Sagron + 1 more +1

Front Matter
10.1016/s0166-5316(23)00058-5

Editorial Board

Nov 01, 2023
Performance Evaluation

Research Article
10.1016/j.peva.2023.102368

A hardware-independent time estimation method for inference process of convolutional layers on GPU

Sep 20, 2023
Performance Evaluation
Chengzhen Meng + 1 more +1

Research Article
10.1016/j.peva.2023.102354

CLC: A cross-level program characterization method

Jul 20, 2023
Performance Evaluation
Li Tang + 1 more +1

Characterization of program execution plays a key role in performance improvement. There are numerous transformations applied to each step that a program takes on its lowering from source code to a compiler intermediate representation to machine language to microarchitecture-specific execution. The unpredictable benefit of each transformation step could lead a notionally superior algorithm to exhibit inferior performance once actually run, and it can be hard to discern which step in the transformation path contradicted the code developer’s assumptions.Conventional approaches to program-execution characterization consider the behavior after only a single one of those steps, which limits the information that can be provided to the user. To help address the issue of myopic views of program execution, this paper presents a novel cross-level characterization approach for understanding the behavior of program execution at different levels in the process of writing, compiling, and running a program. We show that this approach provides a richer view of the sources of performance gains and losses and helps identify program execution in a more accurate manner.

Research Article
3
10.1016/j.peva.2021.102259

Performance analysis of load balancing policies with memory

Nov 03, 2021
Performance Evaluation
Tim Hellemans + 1 more +1