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

An effective dynamic scheduling runtime and tuning system for heterogeneous multi and many-core desktop platforms

A personal computer can be considered as a one-node heterogeneous cluster that simultaneously processes several application tasks. It can be composed by, for example, asymmetric CPU and GPUs. This way, a high-performance heterogeneous platform is built on a desktop for data intensive engineering calculations. In our perspective, a workload distribution over the Processing Units (PUs) plays a key role in such systems. This issue presents challenges since the cost of a task at a PU is non-deterministic and can be affected by parameters not known a priori. This paper presents a context-aware runtime and tuning system based on a compromise between reducing the execution time of engineering applications - due to appropriate dynamic scheduling - and the cost of computing such scheduling applied on a platform composed of CPU and GPUs. Results obtained in experimental case studies are encouraging and a performance gain of 21.77% was achieved in comparison to the static assignment of all tasks to the GPU