The Pacific Research Platform

Smarr, Larry; Crittenden, Camille; DeFanti, Thomas A.; Graham, John H.; Mishin, Dmitry; Moore, Richard W.; Papadopoulos, Philip M.; Würthwein, F.

doi:10.1145/3219104.3219108

Cited by 33 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All services (except AWS IoT) are platform agnostic and can be hosted anywhere. For our particular experimental setup, Redis and S3 were hosted on the Pacific Research Platform (PRP) [28]. The Internet of Things (IoT) service with MQTT messaging and device management was coordinated through Amazon Web Services (AWS).…”

Section: Methodsmentioning

confidence: 99%

“…Affordable, internet-connected devices have already been developed for ECG, EEG, EMG, and heart rate variability monitoring [22–27]. Furthermore, commodity cloud compute from major companies as well as academic coalitions [28] has become widely available and many tools for downstream analysis to process voltage recordings are already offered online [14, 29–32]. However, data acquisition for in vitro cultures remains relatively isolated, as no platform exists to stream data online to link with these analysis infrastructures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

Voitiuk

Geng

Keefe

et al. 2021

Preprint

View full text Add to dashboard Cite

Objective. Neural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. Approach. We developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main Results. We used a Raspberry Pi as the primary processing device and Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting real-time neural activity. Significance. The hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

Voitiuk

Geng

Keefe

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…For workflow coordination, we currently use a simple synchronous dataflow pipeline of Jupyter notebooks running through Kepler WebView [21] on the CHASE-CI through containers. NSF CHASE-CI is a network of fast GPU appliances for machine learning and storage managed through Kubernetes on the high-speed Pacific Research Platform (PRP) [22].…”

Section: Ppods Measurement and Exploration Interfacementioning

confidence: 99%

Toward a Methodology and Framework for Workflow-Driven Team Science

et al. 2019

View full text Add to dashboard Cite

Scientific workflows are powerful tools for management of scalable experiments, often composed of complex tasks running on distributed resources. Existing cyberinfrastructure provides components that can be utilized within repeatable workflows. However, data and computing advances continuously change the way scientific workflows get developed and executed, pushing the scientific activity to be more data-driven, heterogeneous and collaborative. Workflow development today depends on the effective collaboration and communication of a cross-disciplinary team, not only with humans but also with analytical systems and infrastructure. This paper presents a collaboration-centered reference architecture to extend workflow systems with dynamic, predictable and programmable interfaces to systems and infrastructure while bridging the exploratory and scalable activities in the scientific process. We also present a conceptual design towards the development of methodologies and tools for effective workflow-driven collaborations, namely the PPoDS methodology and the SmartFlows Toolkit for smart utilization of workflows in a rapidly evolving cyberinfrastructure ecosystem.

show abstract

“…In the case of commercial Cloud resources, while the performance and cost of compute instances is relatively well documented and understood, the same cannot be said for network links and data movement at large. To address this deficiency, we ran a network characterization campaign in early autumn of 2019, collecting information about peak throughput and latencies in various regions of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP), both between Cloud resources and major DTNs in the Pacific Research Platform (PRP/TNRP) [5], including Open Science Grid (OSG) [6] data federation caches in the Internet2 network backbone [7], and between different regions inside the Clouds themselves.…”

Section: Introductionmentioning

confidence: 99%

Characterizing network paths in and out of the clouds

2020

Self Cite

View full text Add to dashboard Cite

Commercial Cloud computing is becoming mainstream, with funding agencies moving beyond prototyping and starting to fund production campaigns, too. An important aspect of any scientific computing production campaign is data movement, both incoming and outgoing. And while the performance and cost of VMs is relatively well understood, the network performance and cost is not. This paper provides a characterization of networking in various regions of Amazon Web Services, Microsoft Azure and Google Cloud Platform, both between Cloud resources and major DTNs in the Pacific Research Platform, including OSG data federation caches in the network backbone, and inside the clouds themselves. The paper contains both a qualitative analysis of the results as well as latency and peak throughput measurements. It also includes an analysis of the costs involved with Cloud-based networking.

show abstract

The Pacific Research Platform

Cited by 33 publications

References 1 publication

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

Toward a Methodology and Framework for Workflow-Driven Team Science

Characterizing network paths in and out of the clouds

Contact Info

Product

Resources

About