Predictive performance modeling for distributed batch processing using black box monitoring and machine learning
In many domains, the previous decade was characterized by increasing data volumes and
growing complexity of data analyses, creating new demands for batch processing on …
growing complexity of data analyses, creating new demands for batch processing on …
Scheduling techniques for GPU architectures with processing-in-memory capabilities
Processing data in or near memory (PIM), as opposed to in conventional computational units
in a processor, can greatly alleviate the performance and energy penalties of data transfers …
in a processor, can greatly alleviate the performance and energy penalties of data transfers …
Mapping parallelism to multi-cores: a machine learning based approach
Z Wang, MFP O'Boyle - Proceedings of the 14th ACM SIGPLAN …, 2009 - dl.acm.org
The efficient mapping of program parallelism to multi-core processors is highly dependent
on the underlying architecture. This paper proposes a portable and automatic compiler …
on the underlying architecture. This paper proposes a portable and automatic compiler …
Methods of inference and learning for performance modeling of parallel applications
Increasing system and algorithmic complexity combined with a growing number of tunable
application parameters pose significant challenges for analytical performance modeling. We …
application parameters pose significant challenges for analytical performance modeling. We …
Using automated performance modeling to find scalability bugs in complex codes
Many parallel applications suffer from latent performance limitations that may prevent them
from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only …
from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only …
A regression-based approach to scalability prediction
BJ Barnes, B Rountree, DK Lowenthal… - Proceedings of the …, 2008 - dl.acm.org
Many applied scientific domains are increasingly relying on large-scale parallel
computation. Consequently, many large clusters now have thousands of processors …
computation. Consequently, many large clusters now have thousands of processors …
Using machine learning to optimize parallelism in big data applications
In-memory cluster computing platforms have gained momentum in the last years, due to their
ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to …
ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to …
Towards machine learning-based auto-tuning of mapreduce
N Yigitbasi, TL Willke, G Liao… - 2013 IEEE 21st …, 2013 - ieeexplore.ieee.org
MapReduce, which is the de facto programming model for large-scale distributed data
processing, and its most popular implementation Hadoop have enjoyed widespread …
processing, and its most popular implementation Hadoop have enjoyed widespread …
Performance prediction of parallel applications: a systematic literature review
J Flores-Contreras, HA Duran-Limon… - The Journal of …, 2021 - Springer
Different techniques for estimating the execution time of parallel applications have been
studied for the last 25 years. These approaches have proposed different methods for …
studied for the last 25 years. These approaches have proposed different methods for …
Gunther: Search-based auto-tuning of mapreduce
MapReduce has emerged as a very popular programming model for large-scale data
analytics. Despite its industry-wide acceptance, the open source Apache TM Hadoop TM …
analytics. Despite its industry-wide acceptance, the open source Apache TM Hadoop TM …