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pro vyhledávání: '"KANVAR, VINI"'
Autor:
Kanvar, Vini, Khedker, Uday P.
With the growing sizes of data structures allocated in heap, understanding the actual use of heap memory is critically important for minimizing cache misses and reclaiming unused memory. A static analysis aimed at this is difficult because the heap l
Externí odkaz:
http://arxiv.org/abs/2408.12947
For decades, mainframe systems have been vital in enterprise computing, supporting essential applications across industries like banking, retail, and healthcare. To harness these legacy applications and facilitate their reuse, there is increasing int
Externí odkaz:
http://arxiv.org/abs/2408.04230
Enterprises in their journey to the cloud, want to decompose their monolith applications into microservices to maximize cloud benefits. Current research focuses a lot on how to partition the monolith into smaller clusters that perform well across sta
Externí odkaz:
http://arxiv.org/abs/2308.01302
Autor:
Khedker, Uday P., Kanvar, Vini
The original liveness based flow and context sensitive points-to analysis (LFCPA) is restricted to scalar pointer variables and scalar pointees on stack and static memory. In this paper, we extend it to support heap memory and pointer expressions inv
Externí odkaz:
http://arxiv.org/abs/1411.5289
Autor:
Kanvar, Vini, Khedker, Uday P.
Publikováno v:
ACM Computing Surveys, June 2016, Volume 49, Issue 2, Article 29 (http://dl.acm.org/citation.cfm?id=2931098)
Heap data is potentially unbounded and seemingly arbitrary. As a consequence, unlike stack and static memory, heap memory cannot be abstracted directly in terms of a fixed set of source variable names appearing in the program being analysed. This mak
Externí odkaz:
http://arxiv.org/abs/1403.4910
Autor:
KANVAR, VINI1 vini@cse.iitb.ac.in, KHEDKER, UDAY P.1 uday@cse.iitb.ac.in
Publikováno v:
ACM Computing Surveys. Nov2016, Vol. 49 Issue 2, p29-29:47. 47p. 20 Diagrams.
Autor:
Kanvar, Vini, Khedker, Uday P.
Publikováno v:
ACM / SIGPLAN Notices; Sep2017, Vol. 52 Issue 9, p92-103, 12p