Adaptive Data Parallelism for Internet Clients on Heterogeneous Platforms

Il Web moderno ha da molto superato le pagine statiche, limitate alla formattazione HTML e poche immagini. Siamo entrati i un era di Rich Internet Applications come giochi, simulazioni fisiche, rendering di immagini, elaborazione di foto, etc eseguite localmente dai programmi client. Nonostante questo gli attuali linguaggi lato client hanno limitatissime capacità di utilizzare le capacità computazionali della piattaforma, tipicamente eterogenea, sottostante. Presentiamo un DSL (Domain Specific Language) chiamato ASDP (ActionScript Data Parallel) integrato in ActionScript, uno dei linguaggi più popolari per la programmazione lato client e un parente prossimo di JavaScript. ASDP è molto similare ad ActionScript e permette frequentemente di introdurre la programmazione parallela con minime modifiche al codice sorgente. Presentiamo anche un prototipo di un sistema in cui computazioni data parallel possono essere eseguite su CPU o GPU. Il sistema runtime si occuperà di selezionare in modo trasparente la miglior unità computazionale a seconda della computazione, dell'architettura e del carico attuale del sistema. Vengono inoltre valutate le performance del sistema su diversi benchmark, rappresentativi dei seguenti tipi di applicazioni: fisica, elaborazione di immagini, calcolo scientifico e crittografia. Today’s Internet is long past static web pages full of HTML-formatted text sprinkled with an occasional image or animation. We have entered an era of Rich Internet Applications executed locally on Internet clients such as web browsers: games, physics engines, image rendering, photo editing, etc. And yet today’s languages used to program Internet clients have limited ability to tap to the computational capabilities of the underlying, often heterogeneous, platforms. We present how a Domain Specific Language (DSL) can be integrated into ActionScript, one of the most popular scripting languages used to program Internet clients and a close cousin of JavaScript. Our DSL, called ASDP (ActionScript Data Parallel), closely resembles ActionScript and often only minimal changes to existing ActionScript programs are required to enable data parallelism. We also present a prototype of a system, where data parallel workloads can be executed on either CPU or a GPU, with the runtime system transparently selecting the best processing unit, depending on the type of workload as well as the architecture and current load of the execution platform. We evaluate performance of our system on a variety of benchmarks, representing different types of workloads: physics, image processing, scientific computing and cryptography

Measuring and Managing Answer Quality for Online Data-Intensive Services

Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answers without waiting for slow running components to finish. However, data from these slow components could lead to better answers. We propose Ubora, an approach to measure the effect of slow running components on the quality of answers. Ubora randomly samples online queries and executes them twice. The first execution elides data from slow components and provides fast online answers; the second execution waits for all components to complete. Ubora uses memoization to speed up mature executions by replaying network messages exchanged between components. Our systems-level implementation works for a wide range of platforms, including Hadoop/Yarn, Apache Lucene, the EasyRec Recommendation Engine, and the OpenEphyra question answering system. Ubora computes answer quality much faster than competing approaches that do not use memoization. With Ubora, we show that answer quality can and should be used to guide online admission control. Our adaptive controller processed 37% more queries than a competing controller guided by the rate of timeouts.Comment: Technical Repor

An Application Perspective on High-Performance Computing and Communications

We review possible and probable industrial applications of HPCC focusing on the software and hardware issues. Thirty-three separate categories are illustrated by detailed descriptions of five areas -- computational chemistry; Monte Carlo methods from physics to economics; manufacturing; and computational fluid dynamics; command and control; or crisis management; and multimedia services to client computers and settop boxes. The hardware varies from tightly-coupled parallel supercomputers to heterogeneous distributed systems. The software models span HPF and data parallelism, to distributed information systems and object/data flow parallelism on the Web. We find that in each case, it is reasonably clear that HPCC works in principle, and postulate that this knowledge can be used in a new generation of software infrastructure based on the WebWindows approach, and discussed in an accompanying paper

EasyFL: A Low-code Federated Learning Platform For Dummies

Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms are complex to use and require a deep understanding of FL, which imposes high barriers to entry for beginners, limits the productivity of researchers, and compromises deployment efficiency. In this paper, we propose the first low-code FL platform, EasyFL, to enable users with various levels of expertise to experiment and prototype FL applications with little coding. We achieve this goal while ensuring great flexibility and extensibility for customization by unifying simple API design, modular design, and granular training flow abstraction. With only a few lines of code, EasyFL empowers them with many out-of-the-box functionalities to accelerate experimentation and deployment. These practical functionalities are heterogeneity simulation, comprehensive tracking, distributed training optimization, and seamless deployment. They are proposed based on challenges identified in the proposed FL life cycle. Compared with other platforms, EasyFL not only requires just three lines of code (at least 10x lesser) to build a vanilla FL application but also incurs lower training overhead. Besides, our evaluations demonstrate that EasyFL expedites distributed training by 1.5x. It also improves the efficiency of deployment. We believe that EasyFL will increase the productivity of researchers and democratize FL to wider audiences

Middleware-based Database Replication: The Gaps between Theory and Practice

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice. This paper aims to characterize the gap along three axes: performance, availability, and administration. We build on our own experience developing and deploying replication systems in commercial and academic settings, as well as on a large body of prior related work. We sift through representative examples from the last decade of open-source, academic, and commercial database replication systems and combine this material with case studies from real systems deployed at Fortune 500 customers. We propose two agendas, one for academic research and one for industrial R&D, which we believe can bridge the gap within 5-10 years. This way, we hope to both motivate and help researchers in making the theory and practice of middleware-based database replication more relevant to each other.Comment: 14 pages. Appears in Proc. ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, June 200

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer