Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
High-performance stream processing is critical in many sense-and-respond application domainsfrom environmental monitoring to algorithmic trading. In this paper, we focus on language and runtime support for improving the performance of sense-and-respond applications in processing data from high-rate live streams. The central tenets of this work are the programming model, the workload splitting mechanisms, the code generation framework, and the underlying System S middleware and Spade programming model. We demonstrate considerable scalability behavior coupled with low processing latency in a real-world financial trading application. © 2010 Elsevier Inc. All rights reserved.
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
P.C. Yue, C.K. Wong
Journal of the ACM
Arthur Nádas
IEEE Transactions on Neural Networks
Ran Iwamoto, Kyoko Ohara
ICLC 2023