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Cross-Layer Memory Management

Recent trends in computing necessitate an increased focus on power and energy consumption and support for multi-tenant use cases. At the same time, data-intensive computing is placing larger demands on physical memory systems than ever before. However, it is very challenging to obtain precise control over the distribution of memory capacity, bandwidth, and power. Controlling memory power and performance is difficult because these effects depend upon the results of activities across multiple layers of the vertical exection stack, and such results are often not available at any single layer.

To address these challenges, we are investigating approaches to memory management that increase collaboration between layers of the vertical execution stack (i.e. compilers, applications, middleware, operating system, and hardware). We have designed and developed an approach that enables applications to provide guidance to the operating system regarding allocation and recycling of physical memory. We are currently exploring dynamic profiling and analysis to automatically derive and apply beneficial guidance for use during memory management.

Publications: Linux Symposium 2014, VEE 2013

Dynamic Compilation

Programs written in managed languages, such as Java and C#, execute in the context of a virtual machine (VM) (also called runtime system) that compiles program methods at runtime to achieve high-performance emulation. Managed runtime systems need to consider several factors when deciding how, when, or if to compile program methods, including: the compiling speed and code quality produced by the available compiler(s), the execution frequency of individual methods, and the availability of compilation resources. Our research in this area explores tradeoffs involved in selective compilation and the potential of applying iterative search techniques to dynamic compilers.

Publications: TACO 2013, VEE 2013

Exploiting Phase Interactions during Phase Order Search

Program-specific or function-specific compiler optimization phase sequences are universally accepted to achieve better overall performance than any fixed optimization phase ordering. In order to find the best combination of phases to apply to a particular function or program, researchers have developed iterative search techniques to quickly evaluate many different orderings of optimization phases. While such techniques have been shown to be effective, they are also extremely time consuming due to the large number of phase combinations that must be evaluated for each application. We conduct research that aims to reduce the phase ordering search space by identifying and exploiting certain interactions between phases during the search. In addition to speeding up exhaustive iterative searches, this work has led to the invention of a technique that can improve the efficacy of individual optimization phases, as well as novel heuristics that find more effective phase ordering sequences much faster than current approaches.

Publications: CASES 2013 S:P&E 2013, CASES 2010, Masters Thesis (2010), LCTES 2010