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sPIN: High-performance streaming processing in the network

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017

Hoefer, Torsten; Di Girolamo, Salvatore; Taranov, Konstantin; Grant, Ryan E.; Brightwell, Ronald B.

Optimizing communication performance is imperative for large-scale computing because communication overheads limit the strong scalability of parallel applications. Today's network cards contain rather powerful processors optimized for data movement. However, these devices are limited to fixed functions, such as remote direct memory access. We develop sPIN, a portable programming model to offload simple packet processing functions to the network card. To demonstrate the potential of the model, we design a cycle-accurate simulation environment by combining the network simulator Log-GOPSim and the CPU simulator gem5. We implement offloaded message matching, datatype processing, and collective communications and demonstrate transparent full-application speedups. Furthermore, we show how sPIN can be used to accelerate redundant in-memory filesystems and several other use cases. Our work investigates a portable packet-processing network acceleration model similar to compute acceleration with CUDA or OpenCL. We show how such network acceleration enables an eco-system that can significantly speed up applications and system services.

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Enabling Diverse Software Stacks on Supercomputers Using High Performance Virtual Clusters

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Younge, Andrew J.; Pedretti, Kevin P.; Grant, Ryan E.; Gaines, Brian G.; Brightwell, Ronald B.

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed computing models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging software ecosystems.In this paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifically, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, effectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.

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The Portals 4.1 Network Programming Interface

Barrett, Brian W.; Brightwell, Ronald B.; Grant, Ryan E.; Hemmert, Karl S.; Pedretti, Kevin P.; Wheeler, Kyle W.; Underwood, Keith; Riesen, Rolf R.; Maccabe, Arthur B.; Hudson, Trammel H.

This report presents a specification for the Portals 4 networ k programming interface. Portals 4 is intended to allow scalable, high-performance network communication betwee n nodes of a parallel computing system. Portals 4 is well suited to massively parallel processing and embedded syste ms. Portals 4 represents an adaption of the data movement layer developed for massively parallel processing platfor ms, such as the 4500-node Intel TeraFLOPS machine. Sandia's Cplant cluster project motivated the development of Version 3.0, which was later extended to Version 3.3 as part of the Cray Red Storm machine and XT line. Version 4 is tar geted to the next generation of machines employing advanced network interface architectures that support enh anced offload capabilities.

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RMA-MT: A Benchmark Suite for Assessing MPI Multi-threaded RMA Performance

Proceedings - 2016 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016

Dosanjh, Matthew D.; Groves, Taylor G.; Grant, Ryan E.; Brightwell, Ronald B.; Bridges, Patrick G.

Reaching Exascale will require leveraging massive parallelism while potentially leveraging asynchronous communication to help achieve scalability at such large levels of concurrency. MPI is a good candidate for providing the mechanisms to support communication at such large scales. Two existing MPI mechanisms are particularly relevant to Exascale: multi-threading, to support massive concurrency, and Remote Memory Access (RMA), to support asynchronous communication. Unfor-tunately, multi-threaded MPI RMA code has not been extensively studied. Part of the reason for this is that no public benchmarks or proxy applications exist to assess its performance. The contributions of this paper are the design and demonstration of the first available proxy applications and micro-benchmark suite for multi-threaded RMA in MPI, a study of multi-threaded RMA performance of different MPI implementations, and an evaluation of how these benchmarks can be used to test development for both performance and correctness.

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Practical resilient cases for FA-MPI, A transactional fault-Tolerant MPI

Proceedings of the 3rd ExaMPI Workshop at the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2015

Hassani, Amin; Skjellum, Anthony; Bangalore, Purushotham V.; Brightwell, Ronald B.

MPI is insu-cient when confronting failures. FA-MPI (Fault-Aware MPI) provides extensions to the MPI standard de-signed to enable data-parallel applications to achieve re-silience without sacri-cing scalability. FA-MPI introduces transactions as a novel extension to theMPI message-passing model. Transactions support failure detection, isolation, mitigation, and recovery via application-driven policies. To achieve maximum achievable performance of modern ma-chines, overlapping communication and I/O with computa-Tion through non-blocking operations is of growing impor-Tance. Therefore, we emphasize fault-Tolerant, non-blocking communication operations plus a set of nestable lightweight transactional TryBlock API extensions able to exploit sys-Tem and application hierarchy. This strategy enables appli-cations to run to completion with higher probability than nominally. We modi-ed two proxy applications|MiniFE and LULESH|by adding FA-MPI semantics to them. Fi-nally we present performance and overhead results for 1K MPI processes.

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Results 26–50 of 190
Results 26–50 of 190