Knights Landing: Second-Generation Intel Xeon Phi Product

Sodani, Avinash; Gramunt, Roger; Corbal, Jesús; Kim, Ho-Seop; Vinod, Krishna; Chinthamani, Sundaram; Hutsell, Steven T.; Agarwal, Reena; Liu, Yen‐Chen

doi:10.1109/mm.2016.25

Cited by 388 publications

(247 citation statements)

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“…However, some of the KNL results are reported for a specific die to save space whenever a particular configuration does not undergo a significant performance impact, and we focus upon exhibiting more of breadth test cases. In addition, in [41], [42], [43], [44], [45], details of the key aspects of the primary architecture considered herein (i.e., KNL) are illustrated. …”

Section: Methodsmentioning

confidence: 99%

Optimizations of Unstructured Aerodynamics Computations for Many-core Architectures

Farhan

Keyes

2018

IEEE Trans. Parallel Distrib. Syst.

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Section: Methodsmentioning

confidence: 99%

Optimizations of Unstructured Aerodynamics Computations for Many-core Architectures

Farhan

Keyes

2018

IEEE Trans. Parallel Distrib. Syst.

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“…For example, the OpenMP versions were tested over a large range of configurations to find the optimal number of threads. Of particular note, the high bandwidth memory (MCDRAM) for the Knights Landing system was set up to be in flat mode, using Quadrant clustering [22]. This allowed for the memory to be separately addressable and allocates the memory to the closest set of processors.…”

Section: B Resultsmentioning

confidence: 99%

Achieving Performance Portability for a Heat Conduction Solver Mini-Application on Modern Multi-core Systems

Kirk

Mudalige

Reguly

et al. 2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-Modernizing production-grade, often legacy applications to take advantage of modern multi-core and many-core architectures can be a difficult and costly undertaking. This is especially true currently, as it is unclear which architectures will dominate future systems. The complexity of these codes can mean that parallelisation for a given architecture requires significant re-engineering. One way to assess the benefit of such an exercise would be to use mini-applications that are representative of the legacy programs.In this paper, we investigate different implementations of TeaLeaf, a mini-application from the Mantevo suite that solves the linear heat conduction equation. TeaLeaf has been ported to use many parallel programming models, including OpenMP, CUDA and MPI among others. It has also been re-engineered to use the OPS embedded DSL and template libraries Kokkos and RAJA. We use these different implementations to assess the performance portability of each technique on modern multi-core systems.While manually parallelising the application targeting and optimizing for each platform gives the best performance, this has the obvious disadvantage that it requires the creation of different versions for each and every platform of interest. Frameworks such as OPS, Kokkos and RAJA can produce executables of the program automatically that achieve comparable portability. Based on a recently developed performance portability metric, our results show that OPS and RAJA achieve an application performance portability score of 71% and 77% respectively for this application.

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“…As a future work, we aim to extend our model to consider hierarchical storage architectures such as the recently presented KNL Intel CPU [15]. These systems adopt multiple levels of storage, with an hierarchy between two kinds of ram memory, NVM, and SSD and rotational disks.…”

Section: Discussionmentioning

confidence: 99%

Exploiting Key-Value Data Stores Scalability for HPC

Cugnasco

Becerra

Torres

et al. 2017

2017 46th International Conference on Parallel Processing Workshops (ICPPW)

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Abstract-BigData revolutionised the IT industry. It first interested the OLTP systems. Distributed Hash Tables replaced Traditional SQL databases as they guaranteed low response time on simple read/write requests. The second wave recast the data warehousing: map-reduce systems spread as they proved to scale linearly long-running computational workloads on commodity servers. The focus now is on real-time analytics. Being able to analyse massive quantities of data in a short time enables multiple HPC applications and interactive analysis and visualization. In this paper, we study the performance of a system that employs the DHT architecture to achieve fast in local analysis on indexed data. We observed that the number of keys, nodes, and the hardware characteristics strongly influence the actual scalability of the system. Therefore, we developed a mathematical model that allows finding the right system configuration to meet desired performance for each kind of query type. We also show how our model can be used to find the right architecture for each distributed application.

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Knights Landing: Second-Generation Intel Xeon Phi Product

Cited by 388 publications

References 1 publication

Optimizations of Unstructured Aerodynamics Computations for Many-core Architectures

Optimizations of Unstructured Aerodynamics Computations for Many-core Architectures

Achieving Performance Portability for a Heat Conduction Solver Mini-Application on Modern Multi-core Systems

Exploiting Key-Value Data Stores Scalability for HPC

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