Search Results for author: Gustavo Alonso

Found 7 papers, 4 papers with code

Towards Demystifying Serverless Machine Learning Training

1 code implementation17 May 2021 Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang

The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML).

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

no code implementations12 Oct 2020 Wenqi Jiang, Zhenhao He, Shuai Zhang, Thomas B. Preußer, Kai Zeng, Liang Feng, Jiansong Zhang, Tongxuan Liu, Yong Li, Jingren Zhou, Ce Zhang, Gustavo Alonso

MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups.

Recommendation Systems

Benchmarking High Bandwidth Memory on FPGAs

2 code implementations9 May 2020 Zeke Wang, Hongjing Huang, Jie Zhang, Gustavo Alonso

FPGAs are starting to be enhanced with High Bandwidth Memory (HBM) as a way to reduce the memory bandwidth bottleneck encountered in some applications and to give the FPGA more capacity to deal with application state.

Hardware Architecture

Modularis: Modular Relational Analytics over Heterogeneous Distributed Platforms

no code implementations7 Apr 2020 Dimitrios Koutsoukos, Ingo Müller, Renato Marroquín, Gustavo Alonso

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.

Databases

Rumble: Data Independence for Large Messy Data Sets

1 code implementation25 Oct 2019 Stefan Irimescu, Can Berker Cikis, Ingo Müller, Ghislain Fourny, Gustavo Alonso

This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.

JSONiq Query Execution Databases 68N99 H.2.3; C.2.4

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