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 …
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 …
Fast multi-parameter performance modeling
A Calotoiu, D Beckinsale, CW Earl… - 2016 IEEE …, 2016 - ieeexplore.ieee.org
Tuning large applications requires a clever exploration of the design and configuration
space. Especially on supercomputers, this space is so large that its exhaustive traversal via …
space. Especially on supercomputers, this space is so large that its exhaustive traversal via …
Extracting clean performance models from tainted programs
Performance models are well-known instruments to understand the scaling behavior of
parallel applications. They express how performance changes as key execution parameters …
parallel applications. They express how performance changes as key execution parameters …
Selecting efficient cloud resources for hpc workloads
JR Brunetta, E Borin - Proceedings of the 12th IEEE/ACM International …, 2019 - dl.acm.org
Constant advances in CPU, storage, and network virtualization are enabling high-
performance computing (HPC) applications to be efficiently executed on cloud computing …
performance computing (HPC) applications to be efficiently executed on cloud computing …
Learning cost-effective sampling strategies for empirical performance modeling
M Ritter, A Calotoiu, S Rinke, T Reimann… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Identifying scalability bottlenecks in parallel applications is a vital but also laborious and
expensive task. Empirical performance models have proven to be helpful to find such …
expensive task. Empirical performance models have proven to be helpful to find such …
Combining dynamic concurrency throttling with voltage and frequency scaling on task-based programming models
A Navarro Muñoz, A F. Lorenzon… - Proceedings of the 50th …, 2021 - dl.acm.org
Being on the verge of exascale performance has shifted the prioritization of performance in
applications to the inclusion of power-performance efficiency as a primary objective in the …
applications to the inclusion of power-performance efficiency as a primary objective in the …
Performance prediction of parallel applications based on small-scale executions
R Escobar, RV Boppana - 2016 IEEE 23rd International …, 2016 - ieeexplore.ieee.org
Predicting the execution time of parallel applications in High Performance Computing (HPC)
clusters has served different objectives, including helping developers to find relevant areas …
clusters has served different objectives, including helping developers to find relevant areas …
A decoupled kilo-instruction processor
Building processors with large instruction windows has been proposed as a mechanism for
overcoming the memory wall, but finding a feasible and implementable design has been an …
overcoming the memory wall, but finding a feasible and implementable design has been an …
Using hardware counter-based performance model to diagnose scaling issues of HPC applications
Performance diagnosing for HPC applications can be extremely difficult due to their
complicated performance behaviors. One hand, developers used to identify the potential …
complicated performance behaviors. One hand, developers used to identify the potential …