.png){kind=icon}
.png){kind=icon}
Return
RaftLib: A C plus plus template library for high performance stream parallel processing
.png){kind=icon}
PRE
AI
DOI：10.1177/1094342016672542.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Stream processing is a compute paradigm that has been around for decades, yet until recently has failed to garner the same attention as other mainstream languages and libraries (e.g. C++, OpenMP, MPI). Stream processing has great promise: the ability to safely exploit extreme levels of parallelism to process huge volumes of streaming data. There have been many implementations, both libraries and full languages. The full languages implicitly assume that the streaming paradigm cannot be fully exploited in legacy languages, while library approaches are often preferred for being integrable with the vast expanse of extant legacy code. Libraries, however are often criticized for yielding to the shape of their respective languages. RaftLib aims to fully exploit the stream processing paradigm, enabling a full spectrum of streaming graph optimizations, while providing a platform for the exploration of integrability with legacy C/C++ code. RaftLib is built as a C++ template library, enabling programmers to utilize the robust C++ standard library, and other legacy code, along with RaftLib's parallelization framework. RaftLib supports several online optimization techniques: dynamic queue optimization, automatic parallelization, and real-time low overhead performance monitoring.
Keywords:
Stream processing
big-data
C plus plus template library
high performance computing
RaftLib
performance monitoring
parallel processing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.1K
Citations:
1.3K