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).
This year at the High Performance Extreme Computing Conference (HPEC), happening virtually from September 20- 24, Reservoir Labs presents four research papers: Knowledge-guided Tensor Decomposition for Baselining and Anomaly Detection (in collaboration with UMD’s Laboratory for Physical Sciences) An All-at-Once
Dr. Jordi Ros-Giralt, Fellow, and Noah Amsel, Research Engineer at Reservoir Labs, present “Designing Data Center Networks Using Bottleneck Structures,” at the flagship annual conference of the ACM Special Interest Group on Data Communication (SIGCOMM), Tuesday, August 24th, at 12:50pm
Advanced computing hardware and systems require advanced compilers. Learn more about our boutique solutions powered by decades of innovation.
Providing solutions based on rock solid, enterprise-class, high speed network sensing and spectral hypergraph analytics - using advanced algorithms and mathematics to give experts an advantage.
Based on breakthrough research, explore our suite of technologies addressing network flow optimization and packet path acceleration.
Cutting-edge hypergraph analysis technology for Big Data spanning security, finance, geospatial applications, and biology.
Advanced mathematics for new algorithms with asymptotic advantages, for problems in data analytics, sensing, and cognitive systems.
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
632 Broadway, Suite 803
New York, New York 10012
