Data Processing with FPGAs on Modern Architectures

Trends in hardware, the prevalence of the cloud, and the rise of highly demanding applications have ushered an era of specialization that quickly changes how data is processed at scale. These changes are likely to continue and accelerate in the next years as new technologies are adopted and deployed: smart NICs, smart storage, smart memory, disaggregated storage, disaggregated memory, specialized accelerators (GPUS, TPUs, FPGAs), and a wealth of ASICs specifically created to deal with computationally expensive tasks (e.g., cryptography or compression). In this tutorial, we focus on data processing on FPGAs, a technology that has received less attention than, e.g., TPUs or GPUs but that is, however, increasingly being deployed in the cloud for data processing tasks due to the architectural flexibility of FPGAs, along with their ability to process data at line rate, something not possible with other types of processors or accelerators. In the tutorial, we will cover what FPGAs are, their characteristics, their advantages and disadvantages, as well as examples from deployments in the industry and how they are used in various data processing tasks. We will introduce FPGA programming with high-level languages and describe hardware and software resources available to researchers. The tutorial includes case studies borrowed from research done in collaboration with companies that illustrate the potential of FPGAs in data processing and how software and hardware are evolving to take advantage of the possibilities offered by FPGAs. The use cases include: (1) approximated nearest neighbor search, which is relevant to databases and machine learning, (2) remote disaggregated memory, showing how the cloud architecture is evolving and demonstrating the potential for operator offloading and line rate data processing, and (3) recommendation system as an application with tight latency constraints

Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms

The enormous quantity of data produced every day together with advances in data analytics has led to a proliferation of data management and analysis systems. Typically, these systems are built around highly specialized monolithic operators optimized for the underlying hardware. While effective in the short term, such an approach makes the operators cumbersome to port and adapt, which is increasingly required due to the speed at which algorithms and hardware evolve. To address this limitation, we present Modularis, an execution layer for data analytics based on sub-operators, i.e.,composable building blocks resembling traditional database operators but at a finer granularity. To demonstrate the advantages of our approach, we use Modularis to build a distributed query processing system supporting relational queries running on an RDMA cluster, a serverless cloud platform, and a smart storage engine. Modularis requires minimal code changes to execute queries across these three diverse hardware platforms, showing that the sub-operator approach reduces the amount and complexity of the code. In fact, changes in the platform affect only sub-operators that depend on the underlying hardware. We show the end-to-end performance of Modularis by comparing it with a framework for SQL processing (Presto), a commercial cluster database (SingleStore), as well as Query-as-a-Service systems (Athena, BigQuery). Modularis outperforms all these systems, proving that the design and architectural advantages of a modular design can be achieved without degrading performance. We also compare Modularis with a hand-optimized implementation of a join for RDMA clusters. We show that Modularis has the advantage of being easily extensible to a wider range of join variants and group by queries, all of which are not supported in the hand-tuned join.Comment: Accepted at PVLDB vol. 1

Second Aerospace Environmental Technology Conference

The mandated elimination of CFC'S, Halons, TCA, and other ozone depleting chemicals and specific hazardous materials has required changes and new developments in aerospace materials and processes. The aerospace industry has been involved for several years in providing product substitutions, redesigning entire production processes, and developing new materials that minimize or eliminate damage to the environment. These activities emphasize replacement cleaning solvents and their application, verification, compliant coatings including corrosion protection system and removal techniques, chemical propulsion effects on the environment, and the initiation of modifications to relevant processing and manufacturing specifications and standards

AIUCD2017 - Book of Abstracts

Questo volume raccoglie gli abstract degli interventi presentati alla conferenza AIUCD 2017. AIUCD 2017 si è svolta dal 26 al 28 Gennaio 2017 a Roma, ed è stata verrà organizzata dal Digilab, Università Sapienza in cooperazione con il network ITN DiXiT (Digital Scholarly Editions Initial Training Network). AIUCD 2017 ha ospitato anche la terza edizione dell’EADH Day, tenutosi il 25 Gennaio 2017. Gli abstract pubblicati in questo volume hanno ottenuto il parere favorevole da parte di valutatori esperti della materia, attraverso un processo di revisione anonima sotto la responsabilità del Comitato di Programma Internazionale di AIUCD 2017

AIUCD2017 - Book of Abstracts

Questo volume raccoglie gli abstract degli interventi presentati alla conferenza AIUCD 2017. AIUCD 2017 si è svolta dal 26 al 28 Gennaio 2017 a Roma, ed è stata verrà organizzata dal Digilab, Università Sapienza in cooperazione con il network ITN DiXiT (Digital Scholarly Editions Initial Training Network). AIUCD 2017 ha ospitato anche la terza edizione dell’EADH Day, tenutosi il 25 Gennaio 2017. Gli abstract pubblicati in questo volume hanno ottenuto il parere favorevole da parte di valutatori esperti della materia, attraverso un processo di revisione anonima sotto la responsabilità del Comitato di Programma Internazionale di AIUCD 2017