Learning Independent Program and Architecture Representations for Generalizable Performance Modeling

This paper proposes PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional, independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general, efficient, and accurate than previous approaches

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

DeepP: Deep Learning Multi-Program Prefetch Configuration for the IBM POWER 8

[EN] Current multi-core processors implement sophisticated hardware prefetchers, that can be configured by application (PID),to improve the system performance. When running multiple applications, each application can present different prefetch requirements,hence different configurations can be used. Setting the optimal prefetch configuration for each application is a complex task since itdoes not only depend on the application characteristics but also on the interference at the shared memory resources (e.g. memorybandwidth). In his paper, we proposeDeepP, a deep learning approach for the IBM POWER8 that identifies at run-time the bestprefetch configuration for each application in a workload. To this end, the neural network predicts the performance of each applicationunder the studied prefetch configurations by using a set of performance events. The prediction accuracy of the network is improvedthanks to a dynamic training methodology that allows learning the impact of dynamic changes of the prefetch configuration onperformance. At run-time, the devised network infers the best prefetch configuration for each application and adjusts it dynamically.Experimental results show that the proposed approach improves performance, on average, by 5,8%, 6,7%, and 15,8% compared tothe default prefetch configuration across different 6-, 8-, and 10-application workloads, respectively.This work was supported in part by Ministerio de Ciencia, Innovacion y Universidades and the European ERDF under Grant RTI2018-098156-B-C51, in part by Generalitat Valenciana under Grant AICO/2021/266, and in part by Ayudas Contratos predoctorales UPV -subprograma 1 (PAID-01-20). The work of Josue Feliu was supported by a Juan de la Cierva Formacion Contract under Grant FJC2018-036021-I.Lurbe-Sempere, M.; Feliu-Pérez, J.; Petit Martí, SV.; Gómez Requena, ME.; Sahuquillo Borrás, J. (2022). DeepP: Deep Learning Multi-Program Prefetch Configuration for the IBM POWER 8. IEEE Transactions on Computers. 71(10):2646-2658. https://doi.org/10.1109/TC.2021.313999726462658711

Energy-efficient mobile Web computing

Next-generation Web services will be primarily accessed through mobile devices. However, mobile devices are low-performance and stringently energy-constrained. In my dissertation, I propose the design of a high-performance and energy-efficient mobile Web computing substrate. It is a hardware/software co-designed system that delivers satisfactory user quality-of-service (QoS) experience on a mobile energy budget. The key insight is that the traditional interfaces between different Web stacks need to be enhanced with new abstractions that express user QoS experience and that expose architectural-level complexities. On the basis of the enhanced interfaces, I propose synergistic cross-layer optimizations across the processor architecture, Web runtime, programming language, and application layers to maximize the whole system efficiency. The contributions made in this dissertation will likely have a long-term impact because the target application domain, the Web, is becoming a universal mobile development platform, and because our solutions target the fundamental computation layers of the Web domain.Electrical and Computer Engineerin