Research Collection Doctoral Thesis On Large-Scale System Performance Analysis and Software Characterization Author(s): Anghel, Andreea-Simona Publication Date: 2017 Permanent Link: https://doi.org/10.3929/ethz-b-000212482 Rights / License: In Copyright - Non-Commercial Use Permitted This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH Library DISS. ETH NO. 24524 ON LARGE-SCALE SYSTEM PERFORMANCE ANALYSIS AND SOFTWARE CHARACTERIZATION A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by ANDREEA-SIMONA ANGHEL Ing. Sys. Com. Dipl. EPF born on 19.08.1986 citizen of Romania accepted on the recommendation of Prof. Dr. Lothar Thiele, examiner Prof. Dr. Anton Gunzinger, co-examiner Dr. Gero Dittmann, co-examiner 2017 A dissertation submitted to ETH Zurich for the degree of Doctor of Sciences DISS. ETH No. 24524 Prof. Dr. Lothar Thiele, examiner Prof. Dr. Anton Gunzinger, co-examiner Dr. Gero Dittmann, co-examiner Examination date: July 26th, 2017. This work was conducted in the context of the joint ASTRON and IBM DOME project and was funded by the Dutch Ministry of Economische Zaken, and the Province of Drenthe. IBM, Blue Gene, and POWER8 are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. Intel, Xeon and Xeon Phi are trademarks of Intel Corporation in the U.S. and other countries. Other product or service names may be trademarks or service marks of IBM or other companies. To my beloved husband and family Acknowledgements Doing a PhD has been a great experience for me during which I had the chance to learn how to conduct high-quality research, how to write good scientific publications, how to collaborate in an effective manner with engineers and researchers worldwide and to meet some extraordi- nary intelligent people. Here I would like express my appreciation to all of those that helped me during this important stage of my life. First, I would like to extend my sincere gratitude to Prof. Dr. Lothar Thiele for providing me with the opportunity to pursue my doctoral studies under his guidance and for being very supportive throughout the years. I highly appreciate all the discussions that we had during our PhD sessions, which taught me to ask the right research questions and to effectively address them. Furthermore, I would like to thank Prof. Dr. Anton Gunzinger for accepting to co-advise me and for all the feedback during the different stages of my research. Our discussions taught me to pragmatically address a complex research topic and to not forget about the applicability side of research. I sincerely thank Dr. Gero Dittmann for helping me survive professionally and personally throughout my PhD. I highly appreciate his openness and all his guidance throughout these tough years. I am indebted to him for his permanent encouragement and support. I have learned a lot from him, from complex topics such as system modeling to how to efficiently write a technical paper and professionally coordinate a research discussion. Moreover, I would like to express my gratitude to Ronald Luijten for all the support and for always believing in me and to Dr. Ton Engbersen and Dr. Martin Schmatz for allowing me to pursue my graduate studies at IBM Research – Zurich as part of the DOME project. Without them I would have not had the chance to work on this very challenging project. I would like to express special thanks to my DOME P1 colleagues, Dr. Rik Jongerius and Dr. Giovanni Mariani, for all the hard work that we have done together over the last years. We went through many exhausting long technical discussions, but we have always managed to find a good solution. I am also grateful to have built nice memories from the various social events that we have attended together. Within the same project, I also had the opportunity to i Acknowledgements supervise two very good master students, which helped me gain a practical insight into several aspects of compilers and software optimizations. For this and all important lessons that I have learned from our collaboration, I thank Laura Vasilescu and Evelina Dumitrescu. I owe a lot to my current and former colleagues at IBM Research – Zurich, IBM Deutschland R&D GmbH and IBM Research Yorktown (USA) for all the inspiring discussions and for a wonderful work environment throughout the years: Mitch Gusat, Dr. Patricia Sagmeister, Dr. Jonas Weiss, Dr. German Rodriguez Herrera, Georgios Kathareios, Michael Kauffmann, Dr. Cyriel Minkenberg, Dr. Robert Birke, Dr. Peter Altevogt, Dr. Cedric Lichtenau, Dr. Thomas Pflueger, Dr. Jose Moreira and Dr. Jessica Tseng. Special thanks go also to my former and current office-mates, Dr. Anil Kurmus, Dr. Matthias Neugschwandtner, Nathalie Casati, Dr. Florian Auernhammer, Celestine Duenner, Dr. Wolfgang Denzel and Dr. Milos Stanisavljevic. Furthermore, I am grateful to Charlotte Bolliger and Anne-Marie Cromack from the IBM publications department for proofreading and correcting my publications. I have learned a lot from their suggestions and corrections. I would also like to thank Jilly Fotheringham and Jens Poulsen from the IBM IS team for all the help during the past years. I would like to also thank all my DOME colleagues from the Netherlands for all the interesting face-to-face meetings, wonderful social events and for the myriad discussions on innumerable topics, in particular to Albert Jan Boonstra, Bram Veenboer, Leandro Fiorin, Erik Vermij, Chris Broekema, Stefan Wijnholds and Andre Gunst. I would like to thank my parents Liliana and Ilie, my brother Radu and his family for being very patient and coping with my sometimes difficult behavior. They have been a continuous support for me and I thank them for never allowing me to give up. I am also grateful to Grit Abe, Adela Almasi and Mareike Kuehn for their wonderful friendship and for all the great moments that we have created together throughout the last years. Most importantly, I am extremely grateful to my husband Bogdan for his love and permanent support over the years. He has always encouraged me and pushed me to finalize this thesis. Without his constant optimism, I would have probably not arrived at the end of this work. Zurich, 01.05.2017 ii Abstract Over the years, many scientific breakthroughs have only been possible thanks to advances in the field of very large high-performance computer systems. To reach the exascale computing era, these systems will need to further increase their size, performance and energy efficiency. Building an exascale system under stringent power and performance constraints will be a very challenging task for any organization. Project planning requires early estimates of the system size, performance and power consumption. To address these challenges, system designers need holistic methodologies to simultaneously analyze multiple system components and performance metrics. Such methodologies should also be fast so that designers can efficiently analyze a wide range of hardware design points. In this thesis, we devise tools and methods to enable: (1) a qualitative investigation of the performance and power consumption of future large-scale systems, and (2) an efficient ex- ploration of system design points by loading platform-independent software properties into analytic system performance models. We first decouple the software characterization from performance modeling and extract compute and communication properties inherent to appli- cations. Then, we load the hardware-independent software profiles into analytic processor and network models. Such a methodology is useful for system designers at an early design stage to gain insights into system behavior. The main contributions of this thesis are: We introduce PISA (Platform-Independent Software Analysis tool), a framework for • extracting architecture- and ISA-agnostic software profiles from sequential and parallel workloads at native execution time. We illustrate how our framework can be leveraged to extract application signatures that impact the system performance. We analyze if platform-agnostic software profiles can enable analytic modeling of pro- • cessor performance and power consumption. We provide the first study of the accuracy of using ISA-agnostic application signatures with two analytic processor models. The results show that we can achieve an average accuracy of 34%, while preserving the relative performance trends across workloads. We also show that the analytic power model preserves the relative trends across hardware systems. iii Acknowledgements We study how to analytically model the processor branch miss rate, without simulating • the the branch prediction mechanism. We start from a state-of-the-art characterization metric of branch predictability, the branch entropy. We identify the first method to reverse engineer the history size of a branch predictor using branch entropy estimates. We outline the limitations of branch entropy and propose a hardware-independent method to derive analytic performance models of branch predictors. We also introduce a new branch predictability metric that is up to 17 percentage points more accurate than branch entropy. We propose a method for estimating the node injection bandwidth effectively sustained • by a network by taking into account the application’s communication pattern and a net- work specification. We derive analytic bandwidth models for classes of communication patterns (uniform, shift