Stream workloads vary widely, as do proposed stream ar-chitectures. We argue that stream processors should prior-itize efficient temporal and spatial data-parallel execution, while not ignoring support for temporal and spatial kernel-parallel execution. We introduce a new malleable stream architecture with data- and kernel-parallel mechanisms that can be reconfigured as needed by stream applications. 1. The Landscape of Stream Workloads Many applications can be expressed as streams of ele-ments flowing between computational kernels. Although stream programs are more structured than general-purpose programs, they include surprisingly diverse forms of paral-lelism and communication patterns. For example, consider the toy application in Figure...
Streaming applications process possibly infinite streams of data and often have both high throughput...
Part 4: Session 4: Multi-core Computing and GPUInternational audienceHow to parallelize the great am...
Stream processing applications use online analytics to ingest high-rate data sources, process them o...
As multicore architectures enter the mainstream, there is a pressing demand for high-level programmi...
Many application areas for embedded systems, such as DSP, media coding, and image processing, are ba...
Stream processing has a long history as a way of describing and implementing specific kinds of compu...
More and more use cases require fast, accurate, and reliable processing of large volumes of data. To...
Given the ubiquity of multicore processors, there is an acute need to enable the development of scal...
Stream programming is a promising way to expose concurrency to the compiler. A stream program is bui...
Cataloged from PDF version of article.This article addresses the profitability problem associated wi...
Cataloged from PDF version of article.In this paper we study partitioning functions for stream proc...
International audienceTo effectively program parallel architectures it is important to combine a sim...
International audienceStream languages explicitly describe fork-join parallelism and pipelines, offe...
Present-day computing systems have to deal with a continuous growth of data rate and volume. Process...
Stream Processing was recently introduced as a paradigm to easily develop and deploy applications ta...
Streaming applications process possibly infinite streams of data and often have both high throughput...
Part 4: Session 4: Multi-core Computing and GPUInternational audienceHow to parallelize the great am...
Stream processing applications use online analytics to ingest high-rate data sources, process them o...
As multicore architectures enter the mainstream, there is a pressing demand for high-level programmi...
Many application areas for embedded systems, such as DSP, media coding, and image processing, are ba...
Stream processing has a long history as a way of describing and implementing specific kinds of compu...
More and more use cases require fast, accurate, and reliable processing of large volumes of data. To...
Given the ubiquity of multicore processors, there is an acute need to enable the development of scal...
Stream programming is a promising way to expose concurrency to the compiler. A stream program is bui...
Cataloged from PDF version of article.This article addresses the profitability problem associated wi...
Cataloged from PDF version of article.In this paper we study partitioning functions for stream proc...
International audienceTo effectively program parallel architectures it is important to combine a sim...
International audienceStream languages explicitly describe fork-join parallelism and pipelines, offe...
Present-day computing systems have to deal with a continuous growth of data rate and volume. Process...
Stream Processing was recently introduced as a paradigm to easily develop and deploy applications ta...
Streaming applications process possibly infinite streams of data and often have both high throughput...
Part 4: Session 4: Multi-core Computing and GPUInternational audienceHow to parallelize the great am...
Stream processing applications use online analytics to ingest high-rate data sources, process them o...