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Scalable Low Power Issue Queue And Store Queue Design For Superscalar Processors
(2009-03-09)
A Large instruction window is a key requirement to exploit greater Instruction Level Parallelism in out-of-order superscalar processors. Along with the instruction window size, the size of various other structures including ...
A Static Slicing Tool for Sequential Java Programs
(2018-07-28)
A program slice consists of a subset of the statements of a program that can potentially affect values computed at some point of interest. Such a point of interest along with a set of variables is called a slicing criterion. ...
Integrating A New Cluster Assignment And Scheduling Algorithm Into An Experimental Retargetable Code Generation Framework
(2009-03-09)
This thesis presents a new unified algorithm for cluster assignment and acyclic region
scheduling in a partitioned architecture, and preliminary results on its integration into an experimental retargetable code generation ...
Checking Compatability of Programs on Shared Data
(2018-07-28)
A large software system is built by composing multiple programs, possibly developed independently. The component programs communicate by sharing data. Data sharing involves creation of instances of the shared data by one ...
Efficient Instrumentation for Object Flow Profiling
(2018-07-20)
Profiling techniques to detect performance bugs in applications are usually customized to detect a specific bug pattern and involve significant engineering effort. In spite of this effort, many techniques either suffer
from ...
Language Support For Testing CORBA Based Applications
(Indian Institute of Science, 2005-12-07)
Component Based Development has emerged as economical, reusable, scalable way of developing enterprise as well as embedded software applications. Testing distributed component based systems is difficult when third party ...
A Memory Allocation Framework for Optimizing Power Consumption and Controlling Fragmentation
(2018-07-20)
Large physical memory modules are necessary to meet performance demands of today's ap-
plications but can be a major bottleneck in terms of power consumption during idle periods or when systems are running with workloads ...
Reinforcement Learning Algorithms for Off-Policy, Multi-Agent Learning and Applications to Smart Grids
Reinforcement Learning (RL) algorithms are a popular class of algorithms for training an agent to
learn desired behavior through interaction with an environment whose dynamics is unknown to the
agent. RL algorithms ...
Neural Models for Personalized Recommendation Systems with External Information
Personalized recommendation systems use the data generated by user-item interactions (for example, in the form of ratings) to predict different users interests in available items and recommend a set of items or products ...
Algorithms for Fair Clustering
Many decisions today are taken by various machine learning algorithms, hence it is crucial to
accommodate fairness in such algorithms to remove/reduce any kind of bias in the decision.
We incorporate fairness in the ...