Add to ORKGWe make a case for studying the impact of intra-node par-allelism on the performance of data analytics. We identify four performance optimizations that are enabled by an in-creasing number of processing cores on a chip. We discuss the performance impact of these opimizations on two analyt-ics operators and we identify how these optimizations affect each another. Keywords Data analytics; high-performance analytics; intra-node par-allelism 1
Scientific data generated at experimental and observational facilities are increasingly being proces...
Performance is a critical issue in a production system accommodating hundreds of analysis users. Com...
Big data processing has recently gained a lot of attention both from academia and industry. The term...
Distributed execution of real-time data analytics such as event stream processing is the key to scal...
There has been much research devoted to improving the performance of data analytics frameworks, but ...
The increasing use of statistical data analysis in enterprise applications has created an arms race ...
Data on the internet is increasing at an exponential rate. The arms race to build and capitalize on ...
Many organizations today are faced with the challenge of processing and distilling information from ...
The Intel\uae Xeon Phi™ is gaining popularity for high-performance computing (HPC) applications, but...
Performance-analysis tools are indispensable for understanding and optimizing the behavior of parall...
Fundamental data analytics tasks are often simple -- many useful and actionable insights can be garn...
Systems for high performance computing are getting increasingly complex. On the one hand, the number...
We describe an approach to elastically scale the per-formance of a data analytics operator that is p...
While cluster computing frameworks are contin-uously evolving to provide real-time data analysis cap...
Thesis (Ph.D.)--University of Washington, 2018Large-scale data analytics is key to modern science, t...
Scientific data generated at experimental and observational facilities are increasingly being proces...
Performance is a critical issue in a production system accommodating hundreds of analysis users. Com...
Big data processing has recently gained a lot of attention both from academia and industry. The term...
Distributed execution of real-time data analytics such as event stream processing is the key to scal...
There has been much research devoted to improving the performance of data analytics frameworks, but ...
The increasing use of statistical data analysis in enterprise applications has created an arms race ...
Data on the internet is increasing at an exponential rate. The arms race to build and capitalize on ...
Many organizations today are faced with the challenge of processing and distilling information from ...
The Intel\uae Xeon Phi™ is gaining popularity for high-performance computing (HPC) applications, but...
Performance-analysis tools are indispensable for understanding and optimizing the behavior of parall...
Fundamental data analytics tasks are often simple -- many useful and actionable insights can be garn...
Systems for high performance computing are getting increasingly complex. On the one hand, the number...
We describe an approach to elastically scale the per-formance of a data analytics operator that is p...
While cluster computing frameworks are contin-uously evolving to provide real-time data analysis cap...
Thesis (Ph.D.)--University of Washington, 2018Large-scale data analytics is key to modern science, t...
Scientific data generated at experimental and observational facilities are increasingly being proces...
Performance is a critical issue in a production system accommodating hundreds of analysis users. Com...
Big data processing has recently gained a lot of attention both from academia and industry. The term...