Stampede 2

Stanzione, Dan; Barth, William L.; Gaffney, Niall; Gaither, Kelly; Hempel, Chris; Minyard, Tommy; Mehringer, Susan; Wernert, Eric; Tufo, Henry M.; Panda, Dhabaleswar K.; Teller, Patricia J.

doi:10.1145/3093338.3093385

Cited by 42 publications

(7 citation statements)

References 2 publications

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“…For context, it took $8 min (7 min for a one-time encoding of the crystal graphs and 1 min to evaluate the neural network) on a modern laptop computer to predict the band gaps of all 14,482 MOFs in the QMOF-14482 set using the CGCNN model. In stark contrast, it took over 1.5 million hours ($170 years) of computing time on the Stampede2 supercomputer 129,130 to carry out the structure relaxations and compute the band gaps via DFT.…”

Section: Ll Open Accessmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

258

259

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Using a new database of electronic structure properties derived from highthroughput density functional theory calculations for thousands of metal-organic frameworks (MOFs), we benchmark a variety of machine learning models to accurately and rapidly predict MOF band gaps. Unsupervised dimensionality reduction techniques are also used to map the MOF feature space and identify otherwise subtle structure-property relationships. We anticipate that machine learning models derived from this new database will accelerate the discovery of promising MOFs with targeted quantum-chemical properties.

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Section: Ll Open Accessmentioning

confidence: 99%

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Rosen

Iyer

Ray

et al. 2021

Matter

258

259

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“…Graphs All CPU experiments were conducted on the Stampede KNL Cluster (Stampede2) [64] at the Texas Advanced Computing Center [6], a distributed cluster connected through Intel Omni-Path Architecture (peak bandwidth of 100Gbps). Each node has a KNL processor with 68 1.4 GHz cores running four hardware threads per core, 96GB of DDR4 RAM, and 16GB of MC-DRAM.…”

Section: Experimental Setup Benchmarks and Inputmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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This paper introduces a new approach to building distributedmemory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for * Both authors contributed equally.

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“…Experiments were conducted on the Stampede [60] and Wrangler clusters at the Texas Advanced Computing Center [5]. The configurations used are listed in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…Fault tolerant graph-analytical systems: Many systems for distributed graph analytics [2, 25,35,37,46,47,49,57,58,64,65,68] support fault tolerance transparently. Greft [47] is the only one that tolerates Byzantine (data corruption) faults; the rest tolerate only fail-stop faults. Section 2.2 contrasts Phoenix with other fail-stop fault tolerant systems in detail.…”

Section: Related Workmentioning

confidence: 99%

Phoenix

Dathathri

Gill

Hoang

et al. 2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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This paper presents Phoenix, a communication and synchronization substrate that implements a novel protocol for recovering from fail-stop faults when executing graph analytics applications on distributed-memory machines. The standard recovery technique in this space is checkpointing, which rolls back the state of the entire computation to a state that existed before the fault occurred. The insight behind Phoenix is that this is not necessary since it is sufficient to continue the computation from a state that will ultimately produce the correct result. We show that for graph analytics applications, the necessary state adjustment can be specified easily by the programmer using a thin API supported by Phoenix. Phoenix has no observable overhead during fault-free execution, and it is resilient to any number of faults while guaranteeing that the correct answer will be produced at the end of the computation. This is in contrast to other systems in this space which may either have overheads even during fault-free execution or produce only approximate answers when faults occur during execution. We incorporated Phoenix into D-Galois, the state-of-theart distributed graph analytics system, and evaluated it on two production clusters. Our evaluation shows that in the absence of faults, Phoenix is ∼ 24× faster than GraphX, which provides fault tolerance using the Spark system. Phoenix also outperforms the traditional checkpoint-restart technique implemented in D-Galois: in fault-free execution, Phoenix has no observable overhead, while the checkpointing technique has 31% overhead. Furthermore, Phoenix mostly outperforms * Both authors contributed equally.

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Stampede 2

Cited by 42 publications

References 2 publications

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Phoenix

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