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

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning

Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enables practitioners and researchers to unleash the potential of these models across a wide range of downstream tasks. As these repositories keep growing exponentially, efficiently selecting a good model for the task at hand becomes paramount. By carefully comparing various selection and search strategies, we realize that no single method outperforms the others, and hybrid or mixed strategies can be beneficial. Therefore, we propose SHiFT, the first downstream task-aware, flexible, and efficient model search engine for transfer learning. These properties are enabled by a custom query language SHiFT-QL together with a cost-based decision maker, which we empirically validate. Motivated by the iterative nature of machine learning development, we further support efficient incremental executions of our queries, which requires a careful implementation when jointly used with our optimizations

IX Open-source version 1.1 - Deployment and Evaluation Guide

This Technical Report provides the deployment and evaluation guide of the IX dataplane operating system, as of its first open-source release on May 27, 2016. To facilitate the reproduction of our results, we include in this report the precise steps needed to install, deploy and configure IX and its workloads. We reproduce all benchmarks previously published in two peer-reviewed publications at OSDI '14 and SoCC '15 using this up-to-date, open-source code base

A survey and classification of software-defined storage systems

The exponential growth of digital information is imposing increasing scale and efficiency demands on modern storage infrastructures. As infrastructure complexity increases, so does the difficulty in ensuring quality of service, maintainability, and resource fairness, raising unprecedented performance, scalability, and programmability challenges. Software-Defined Storage (SDS) addresses these challenges by cleanly disentangling control and data flows, easing management, and improving control functionality of conventional storage systems. Despite its momentum in the research community, many aspects of the paradigm are still unclear, undefined, and unexplored, leading to misunderstandings that hamper the research and development of novel SDS technologies. In this article, we present an in-depth study of SDS systems, providing a thorough description and categorization of each plane of functionality. Further, we propose a taxonomy and classification of existing SDS solutions according to different criteria. Finally, we provide key insights about the paradigm and discuss potential future research directions for the field.This work was financed by the Portuguese funding agency FCT-Fundacao para a Ciencia e a Tecnologia through national funds, the PhD grant SFRH/BD/146059/2019, the project ThreatAdapt (FCT-FNR/0002/2018), the LASIGE Research Unit (UIDB/00408/2020), and cofunded by the FEDER, where applicable

DeltaZip: Multi-Tenant Language Model Serving via Delta Compression

Fine-tuning large language models (LLMs) for downstream tasks can greatly improve model quality, however serving many different fine-tuned LLMs concurrently for users in multi-tenant environments is challenging. Dedicating GPU memory for each model is prohibitively expensive and naively swapping large model weights in and out of GPU memory is slow. Our key insight is that fine-tuned models can be quickly swapped in and out of GPU memory by extracting and compressing the delta between each model and its pre-trained base model. We propose DeltaZip, an LLM serving system that efficiently serves multiple full-parameter fine-tuned models concurrently by aggressively compressing model deltas by a factor of $6\times$ to $8\times$ while maintaining high model quality. DeltaZip increases serving throughput by $1.5\times$ to $3\times$ and improves SLO attainment compared to a vanilla HuggingFace serving system

Analyzing Vectorized Hash Tables Across CPU Architectures

Data processing systems often leverage vector instructions to achieve higher performance. When applying vector instructions, an often overlooked data structure is the hash table, even though it is fundamental in data processing systems for operations such as indexing, aggregating, and joining. In this paper, we characterize and evaluate three fundamental vectorized hashing schemes, vectorized linear probing (VLP), vectorized fingerprinting (VFP), and bucket-based comparison (BBC). We implement these hashing schemes on the x86, ARM, and Power CPU architectures, as modern database systems must provide efficient implementations for multiple platforms due to the continuously increasing hardware heterogeneity. We present various implementation variants and platform-specific optimizations, which we evaluate for integer keys, string keys, large payloads, skewed distributions, and multiple threads. Our extensive evaluation and comparison to three scalar hashing schemes on four servers shows that BBC outperforms scalar linear probing by a factor of more than 2x, while also scaling well to high load factors. We find that vectorized hashing schemes come with caveats that need to be considered, such as the increased engineering overhead, differences between CPUs, and differences between vector ISAs, such as AVX and AVX-512, which impact performance. We conclude with key findings for vectorized hashing scheme implementations.ISSN:2150-809

Rethinking Serverless Computing: from the Programming Model to the Platform Design

Serverless computing offers a number of advantages over conventional, Virtual Machine (VM) based deployments on the cloud, e.g., greater elasticity, simplicity of use and management, finer granularity billing, and rapid deployment and start up times. Naturally, there is a growing interest in exploring how to run applications in this new environment and data analytics is not an exception. Unfortunately, current serverless platforms are limited along several dimensions, which makes things quite difficult from the perspective of data analytics. In this paper we explore what serverless has to offer today, what is missing, and what can be done to make serverless a better computing platform in general and for data analytics in particular.ISSN:1613-007