Companion of the 2023 ACM/SPEC International Conference on Performance Engineering最新文献

{"title":"Visualizing Runtime Evolution Paths in a Multidimensional Space (Work In Progress Paper)","authors":"H. Tarner, Fabian Beck","doi":"10.1145/3578245.3585031","DOIUrl":"https://doi.org/10.1145/3578245.3585031","url":null,"abstract":"Runtime data of software systems is often of multivariate nature, describing different aspects of performance among other characteristics, and evolves along different versions or changes depending on the execution context. This poses a challenge for visualizations, which are typically only two- or three-dimensional. Using dimensionality reduction, we project the multivariate runtime data to 2D and visualize the result in a scatter plot. To show changes over time, we apply the projection to multiple timestamps and connect temporally adjacent points to form trajectories. This allows for cluster and outlier detection, analysis of co-evolution, and finding temporal patterns. While projected temporal trajectories have been applied to other domains before, we use it to visualize software evolution and execution context changes as evolution paths. We experiment with and report results of two application examples: (I) the runtime evolution along different versions of components from the Apache Commons project, and (II) a benchmark suite from scientific visualization comparing different rendering techniques along camera paths.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124466788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICPE'23 GraphSys Workshop Chairs Introduction (Welcome)","authors":"A. Iosup, R.-C. Prodan","doi":"10.1145/3578245.3585328","DOIUrl":"https://doi.org/10.1145/3578245.3585328","url":null,"abstract":"It is our great pleasure to welcome you to the 2023 ACM/SPEC Workshop on Serverless, Extreme-Scale, and Sustainable Graph Processing Systems. This is the first such workshop, aiming to facilitate the exchange of ideas and expertise in the broad field of high-performance large-scale graph processing. Graphs and GraphSys - The use, interoperability, and analytical exploitation of graph data are essential for modern digital economies. Today, thousands of computational methods (algorithms) and findable, accessible, interoperable, and reusable (FAIR) graph datasets exist. However, current computational capabilities lag when faced with the complex workflows involved in graph processing, the extreme scale of existing graph datasets, and the need to consider sustainability metrics in graph-processing operations. Needs are emerging for graph-processing platforms to provide multilingual information processing and reasoning based on the massive graph representation of extreme data in the form of general graphs, knowledge graphs, and property graphs. Because graph workloads and graph datasets are strongly irregular, and involve one or several big data \"Vs\" (e.g., volume, velocity, variability, vicissitude), the community needs to reconsider traditional approaches in performance analysis and modeling, system architectures and techniques, serverless and \"as a service\" operation, real-world and simulation-driven experimentation, etc., and provide new tools and instruments to address emerging challenges in graph processing. Graphs or linked data are crucial to innovation, competition, and prosperity and establish a strategic investment in technical processing and ecosystem enablers. Graphs are universal abstractions that capture, combine, model, analyze, and process knowledge about real and digital worlds into actionable insights through item representation and interconnectedness. For societally relevant problems, graphs are extreme data that require further technological innovations to meet the needs of the European data economy. Digital graphs help pursue the United Nations Sustainable Development Goals (UN SDG) by enabling better value chains, products, and services for more profitable or green investments in the financial sector and deriving trustworthy insight for creating sustainable communities. All science, engineering, industry, economy, and society-at-large domains can leverage graph data for unique analysis and insight, but only if graph processing becomes easy to use, fast, scalable, and sustainable. GraphSys is a cross-disciplinary meeting venue focusing on state-of-the-art and the emerging (future) graph processing systems. We invite experts and trainees in the field, across academia, industry, governance, and society, to share experience and expertise leading to a shared body of knowledge, to formulate together a vision for the field, and to engage with the topics to foster new approaches, techniques, and solutions.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128805270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding and Improving Software Performance at MongoDB","authors":"D. Daly","doi":"10.1145/3578245.3584855","DOIUrl":"https://doi.org/10.1145/3578245.3584855","url":null,"abstract":"It is important for developers to understand the performance of a software project as they develop new features, fix bugs, and try to generally improve the product. At MongoDB we have invested in building a performance infrastructure to support our developers. The infrastructure automates the provisioning of systems under test, the running of performance tests against those systems, collecting many metrics from the tests and system under test, and making sense of all the results. Our performance infrastructure and processes are continually changing. As the system has become more powerful we have used it more and more: adding new tests, new configurations, and new measurements. Tools and processes that work on one scale of use start to break down at higher scales and we must adapt and update. If we do a good job, we keep pace with the rising constraints. If we do a great job, we make the system fundamentally better even as we scale the system. In this talk we describe our performance testing environment at MongoDB and its evolution over time. The core of our environment is a focus on automating everything, integrating into our continuous integration (CI) system (Evergreen), controlling as many factors as possible, and making everything as repeatable and consistent as possible. After describing that core, we will discuss the scaling challenges we have faced, before relating what we have done to address those scaling challenges and improve the system overall.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122955620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Progress on Enhancing Existing Software Engineering Courses to Cultivate Performance Awareness","authors":"A. Bondi, Lu Xiao","doi":"10.1145/3578245.3584352","DOIUrl":"https://doi.org/10.1145/3578245.3584352","url":null,"abstract":"Software engineering and computer science courses are frequently focused on particular areas in a way that neglects such cross-cutting quality attributes as performance, reliability, and security. We will describe the progress we have made in developing enhancements to some of our existing software engineering courses to draw attention and even lay the foundations of an awareness of performance considerations in the software development life cycle. In doing so, we wish to make performance considerations integral to the software engineering mindset while avoiding the need to remove current material from our existing courses. This work is part of an NSF-funded project for undergraduate curriculum development.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"66 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131491185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HotCloudPerf'23 Workshop Chairs' Welcome","authors":"Klervie Toczé, Cristina L. Abad, N. Herbst, A. Iosup","doi":"10.1145/3578245.3584725","DOIUrl":"https://doi.org/10.1145/3578245.3584725","url":null,"abstract":"It is our great pleasure to welcome you to the 2023 edition of the Workshop on Hot Topics in Cloud Computing Performance - HotCloudPerf 2023. Cloud computing is emerging as one of the most profound changes in the way we build and use IT. The use of global services in public clouds is increasing, and the lucrative and rapidly growing global cloud market already supports over 1 million IT-related jobs. However, it is currently challenging to make the IT services offered by public and private clouds performant (in an extended sense) and efficient. Emerging architectures, techniques, and real-world systems include interactions with the computing continuum, serverless operation, everything as a service, complex workflows, auto-scaling and -tiering, etc. It is unclear to which extent traditional performance engineering, software engineering, and system design and analysis tools can help with understanding and engineering these emerging technologies. The community needs practical tools and powerful methods to address hot topics in cloud computing performance. Responding to this need, the HotCloudPerf workshop proposes a meeting venue for academics and practitioners, from experts to trainees, in the field of cloud computing performance. The workshop aims to engage this community and to lead to the development of new methodological aspects for gaining a deeper understanding not only of cloud performance, but also of cloud operation and behavior, through diverse quantitative evaluation tools, including benchmarks, metrics, and workload generators. The workshop focuses on novel cloud properties such as elasticity, performance isolation, dependability, and other non-functional system properties, in addition to classical performance-related metrics such as response time, throughput, scalability, and efficiency.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133110707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Acceleration of FaaS Using Remote GPU Virtualization","authors":"Diana M. Naranjo Delgado, Manuel Contreras, Germán Moltó, Sebastián Risco, I. Blanquer, Javier Prades, F. Silla","doi":"10.1145/3578245.3584933","DOIUrl":"https://doi.org/10.1145/3578245.3584933","url":null,"abstract":"Serverless computing and, in particular, Function as a Service (FaaS) has introduced novel computational approaches with its highly-elastic capabilities, per-millisecond billing and scale-to-zero capacities, thus being of interest for the computing continuum. Services such as AWS Lambda allow efficient execution of event-driven short-lived bursty applications, even if there are limitations in terms of the amount of memory and the lack of GPU support for accelerated execution. To this aim, this paper analyses the suitability of including GPU support in AWS Lambda through the rCUDA middleware, which provides CUDA applications with remote GPU execution capabilities. A reference architecture for data-driven accelerated processing is introduced, based on elastic queues and event-driven object storage systems to manage resource contention and GPU scheduling. The benefits and limitations are assessed through a use case of sequence alignment. The results indicate that, for certain scenarios, the usage of remote GPUs in AWS Lambda represents a viable approach to reduce the execution time.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114504470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Levelling up Performance and Performance Skills at MongoDB","authors":"D. Daly","doi":"10.1145/3578245.3584354","DOIUrl":"https://doi.org/10.1145/3578245.3584354","url":null,"abstract":"MongoDB has invested in developing a performance infrastructure and a corresponding performance culture. All development engineers are expected to improve MongoDB performance, through adding performance tests, optimizing code, and fixing performance regressions. Investing in the infrastructure is clear: we develop and support tools to make it easy to track performance changes and improve performance. Investing in the culture includes formal and informal training. The training must ultimately both support strong developers with very limited performance backgrounds as well as developing our future performance experts.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Supervised Event Predictions with Graph Networks","authors":"Peter Nemec, Gregor Leban, Jože M. Rožanec, M. Grobelnik","doi":"10.1145/3578245.3585332","DOIUrl":"https://doi.org/10.1145/3578245.3585332","url":null,"abstract":"This paper describes how we envision classifying events into the United Nations Sustainable Development Goals (SDGs) by utilizing machine learning techniques on global news data. We propose extracting data from a media intelligence platform using an ontology and a classifier to assign each event to its corresponding SDG. To minimize the labeling effort, a few-shot classification approach is employed. Additionally, a labeling tool is developed to facilitate event analysis and assign labels accurately. We envision this approach could be used for analyzing media events at a large scale and track progress towards the SDGs.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114009854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OSCAR-P and aMLLibrary: Performance Profiling and Prediction of Computing Continua Applications","authors":"Enrico Galimberti, Bruno Guindani, Federica Filippini, Hamta Sedghani, D. Ardagna, Germán Moltó, M. Caballer","doi":"10.1145/3578245.3584941","DOIUrl":"https://doi.org/10.1145/3578245.3584941","url":null,"abstract":"This paper proposes an auto-profiling tool for OSCAR, an open-source platform able to support serverless computing in cloud and edge environments. The tool, named OSCAR-P, is designed to automatically test a specified application workflow on different hardware and node combinations, obtaining relevant information on the execution time of the individual components. It then uses the collected data to build performance models using machine learning, making it possible to predict the performance of the application on unseen configurations. The preliminary evaluation of the performance models accuracy is promising, showing a mean absolute percentage error for extrapolation lower than 10%.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114694103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Sustainable Serverless Processing of Massive Graphs on the Computing Continuum","authors":"Reza Farahani, Dragi Kimovski, S. Ristov, A. Iosup, R.-C. Prodan","doi":"10.1145/3578245.3585331","DOIUrl":"https://doi.org/10.1145/3578245.3585331","url":null,"abstract":"With the ever-increasing volume of data and the demand to analyze and comprehend it, graph processing has become an essential approach for solving complex problems in various domains, like social networks, bioinformatics, and finance. Despite the potential benefits of current graph processing platforms, they often encounter difficulties supporting diverse workloads, models, and languages. Moreover, existing platforms suffer from limited portability and interoperability, resulting in redundant efforts and inefficient resource and energy utilization due to vendor and even platform lock-in. To bridge the aforementioned gaps, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. In this paper, we briefly introduce the Graph-Massivizer platform. We explore how the emerging serverless computing paradigm can be leveraged to devise a scalable graph analytics tool over a codesigned computing continuum infrastructure. Finally, we sketch seven crucial research questions in our design and outline three ongoing and future research directions for addressing them.","PeriodicalId":190503,"journal":{"name":"Companion of the 2023 ACM/SPEC International Conference on Performance Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126742989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}