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Ulmer, Craig D.

Publications and source records attributed to Ulmer, Craig D..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Performance Characteristics of the BlueField-2 SmartNIC

High-performance computing (HPC) researchers have long envisioned scenarios where application workflows could be improved through the use of programmable processing elements embedded in the network fabric. Recently, vendors have introduced programmable Smart Network Interface Cards (SmartNICs) that enable computations to be offloaded to the edge of the network. There is great interest in both the HPC and high-performance data analytics (HPDA) communities in understanding the roles these devices may play in the data paths of upcoming systems. This paper focuses on characterizing both the networking and computing aspects of NVIDIA’s new BlueField-2 SmartNIC when used in a 100Gb/s Ethernet environment. For the networking evaluation we conducted multiple transfer experiments between processors located at the host, the SmartNIC, and a remote host. These tests illuminate how much effort is required to saturate the network and help estimate the processing headroom available on the SmartNIC during transfers. For the computing evaluation we used the stress-ng benchmark to compare the BlueField-2 to other servers and place realistic bounds on the types of offload operations that are appropriate for the hardware. Our findings from this work indicate that while the BlueField-2 provides a flexible means of processing data at the network’s edge, great care must be taken to not overwhelm the hardware. While the host can easily saturate the network link, the SmartNIC’s embedded processors may not have enough computing resources to sustain more than half the expected bandwidth when using kernel-space packet processing. From a computational perspective, encryption operations, memory operations under contention, and on-card IPC operations on the SmartNIC perform significantly better than the general-purpose servers used for comparisons in our experiments. Therefore, applications that mainly focus on these operations may be good candidates for offloading to the SmartNIC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Carnac for Emulytics (HPC Annual Report V.1.0)

Carnac, located at Sandia's California site, is an institutional cluster for Emulytics that provides security researchers with resources to model enterprise computer networks and evaluate how resilient they are from attacks. While multiple Emulytics cluster computers have been built at Sandia, Carnac is the first system that was developed as an institutional resource that can be shared among different groups with disparate requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data Services for Visualization and Analysis - ASC Level II Milestone (7186)

A new in transit Data Service is presented and compared to the traditional file-based workflow and the newly refactored in situ Catalyst workflow. Each workflow is enabled by the IOSS mesh interface equipped with data management layers for Exodus and CGNS (file-based), Catalyst (in situ), and FAODEL (in transit). FAODEL is a distributed object store that can transmit data across MPI allocations. Catalyst is a Para View-based visualization capability developed as part of the CSSE Data Services effort. The workflows considered here take SPARC data into Catalyst for visualization post-processing. Although still in unoptimized form, we show that the in transit approach is a viable alternative to file-based and in situ workflows and offers several advantages to both simulation and post-processing developers. Since IOSS is a mature interface with wide adoption across Sandia and externally, each workflow can be reconfigured to use different simulations that generate mesh data and post-processing tools that consume it.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

RoCE: Promising Technology for Ethernet as a High Performance Networking Fabric

Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) has the potential to provide performance that rivals traditional high performance fabrics. If this potential proves out, significant impacts on system procurement decisions could follow. This work provides a series of small scale performance results which are used to compare and contrast the performance of RoCE-enabled Ethernet with TCP-based Ethernet and an HPC network. Additionally, a discussion of the maturity of RoCE firmware/software stacks and documentation is provided along with useful approaches for probing performance. A detailed description of two experimental setups known to have good RoCE performance is given, including step-by-step configuration and the exact hardware and software revisions employed. At small scales, RoCE is found to have significant performance advantages over "out-of-the-box" TCP protocols and is competitive with state-of-the-art high performance networks. Further examination of RoCE using a wider array of benchmarks and at greater scale is warranted.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

An Initial Investigation of the Design Challenges Associated with Reliable 100GigE Packet Capture

Network security researchers often rely on Emulytics TM to provide a way to evaluate the safety and security of real world systems. This work involves running a large number of virtual machines on a distributed platform to observe how software and hardware will respond to different types of attacks. While Emulytics TM software such as minimega provide a scalable system for conducting experiments, the sheer volume of network traffic produced in an experiment can easily exceed the rate at which data can be recorded for offline analysis. As such, researchers must perform live analytics, narrow their monitoring scope or accept that they must run an experiment multiple times to capture all the information they require. In support of Sandia's commitment to Emulytics TM , we are developing new storage components for the Carlin cluster that will enable researchers to capture significantly more network traffic from their experiments. This report provides a summary of Haoda Wang's initial investigation of how new AMD Epyc storage nodes can be adapted to perform packet capture at 100Gbps speeds with minimal loss. This work found that the NVMe storage capabilities of the Epyc architecture are suitable for capturing 100Gbps Ethernet traffic. While capturing traffic with existing libraries was surprisingly challenging, we were able to develop a DPDK-based software tool that recorded network traffic to disk with minimal packet loss.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

ASC ATDM Level 2 Milestone #6358: Assess Status of Next Generation Components and Physics Models in EMPIRE

This report documents the outcome from the ASC ATDM Level 2 Milestone 6358: Assess Status of Next Generation Components and Physics Models in EMPIRE. This Milestone is an assessment of the EMPIRE (ElectroMagnetic Plasma In Realistic Environments) application and three software components. The assessment focuses on the electromagnetic and electrostatic particle-in-cell solutions for EMPIRE and its associated solver, time integration, and checkpoint-restart components. This information provides a clear understanding of the current status of the EMPIRE application and will help to guide future work in FY19 in order to ready the application for the ASC ATDM L1 Milestone in FY20. It is clear from this assessment that performance of the linear solver will have to be a focus in FY19.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

SNL ATDM Data and Visualization: IOSS and FAODEL

The SNL ATDM Data and Visualization project is developing data management software to improve how applications store and exchange large datasets efficiently on Exascale platforms. The data portion of this project is composed of two related efforts: (1) production work focused on improving Sandia's IOSS library for mesh datasets and (2) research work focused on developing new communication software named FAODEL that enables applications in a workflow to exchange data more efficiently.

97 MATHEMATICS AND COMPUTING↗