On October 27-28, the 13th Annual Concurrent Collections (CnC) Workshop will be held at the SUNY Global Center in NYC, emphasizing parallel programming models for mainstream programmers that philosophically differs from other approaches. The morning of Thursday, October 28, Reservoir
Reservoir Labs is proud to sponsor Suricon 2021, where Dr. Jordi Ros-Giralt, Fellow at Reservoir Labs, presents “Enabling Suricata in the Cloud at Scale Using DPDK,” (abstract below). This year’s annual Suricon conference is hosted in Boston, Massachusetts, October 21-23,
Dr. Harper Langston, Technical Lead, Algorithms & Sensors, presents a poster, “Fast Multipole Method (FMM)-based Particle Accelerator Simulations in the Context of Tune Depression Studies,” at the 18th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2021).
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As part of the MACH-B (Multipole Accelerator Codes for Hadron Beams) project, Reservoir Labs has developed a\ Fast Multipole Method (FMM [1–7])-based tool for higher fidelity modeling of particle accelerators for high-energy physics within Fermilab’s Synergia [8, 9] simulation pack- age. We present results from our implementations with a focus
CANDECOMP/PARAFAC (CP) tensor decomposition is a popular method for detecting latent behaviors in real– world data sets. As data sets grow larger and more elaborate, more sophisticated CP decomposition algorithms are required to enable these discoveries. Data sets from many applications can be represented as count tensors. To decompose count
Reservoir Labs, through a project titled MACH- B (Multipole Accelerator Codes for Hadron Beams), is developing a Fast Multipole Method [1]–[7] (FMM)-based tool for higher fidelity modeling of particle accelerators for high-energy physics with an initial focus on the next generation of Fermi- lab’s Synergia simulation package [8]. MACH-B incorporates
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