On March 26 at 9:00pm CET / 4:00pm ET, Reservoir Labs’ Dr. Benoit Meister presents recent research on “Trading off Latency for Memory Bandwidth in Spatial Architectures using the Polyhedral Model” at the International Conference on HPC & Simulation (HPCS),
On January 20, 2021 at the 11th International Workshop on Polyhedral Compilation Techniques (IMPACT), Reservoir Labs presents its latest paper, “Static Versioning in the Polyhedral Model,” authored by Adithya Dattatri and Dr. Benoit Meister. This workshop is in conjunction
This year’s Supercomputing Conference (SC20) is taking place virtually from November 9 – 17. Reservoir Labs’ Networking team shares its latest research on Thursday, November 12. During SCinet’s Network Research Exhibition (NRE) Research Overview, Sruthi Yellamraju, Principal Engineer, showcases
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In a system for storing in memory a tensor that includes at least three modes, elements of the tensor are stored in a mode-based order for improving locality of references when the elements are accessed during an operation on the tensor. To facilitate efficient data reuse in a tensor transform
In a system for efficiently detecting large/elephant flows in a network, the rate at which the received packets are sampled is adjusted according to a top flow detection likelihood computed for a cache of flows identified in the arriving network traffic. After observing packets sampled from the network, Dirichlet-Categorical inference
We present an approach to enhancing the optimization process in a polyhedral compiler by introducing compile-time versioning, i.e., the production of several versions of optimized code under varying assumptions on its run-time parameters. We illustrate this process by enabling versioning in the polyhedral processor placement pass. We propose an efficient
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