Thu 29 Oct 2015 14:37 - 15:00 at Grand Station 1 - 7. Runtime Chair(s): Michael Pradel

Performance and energy efficiency in memory have become critically important for a wide range of computing domains. However, it is difficult to control and optimize memory power and performance because these effects depend upon activity across multiple layers of the vertical execution stack. To address this challenge, we construct a novel and collaborative framework that employs object placement, cross-layer communication, and page-level management to effectively distribute application objects in the DRAM hardware to achieve desired power/performance goals. In this work, we describe the design and implementation of our framework, which is the first to integrate automatic object profiling and analysis at the application layer with fine-grained management of memory hardware resources in the operating system. We demonstrate the utility of our framework by employing it to more effectively control memory power consumption. We design a custom memory-intensive workload to show the potential of our approach. Next, we develop sampling and profiling-based analyses and modify the code generator in the HotSpot VM to understand object usage patterns and automatically determine and control the placement of hot and cold objects in a partitioned VM heap. This information is communicated to the operating system, which uses it to map the logical application pages to the appropriate DRAM ranks according to user-defined provisioning goals. We evaluate our framework and find that it achieves our test goal of significant DRAM energy savings across a variety of workloads, without any source code modifications or recompilations.

Thu 29 Oct

Displayed time zone: Eastern Time (US & Canada) change

13:30 - 15:00
7. RuntimeOOPSLA at Grand Station 1
Chair(s): Michael Pradel TU Darmstadt, Germany
13:30
22m
Talk
Accurate Profiling in the Presence of Dynamic CompilationOOPSLA Artifact
OOPSLA
Yudi Zheng University of Lugano, Lubomír Bulej Università della Svizzera italiana, Walter Binder University of Lugano
DOI
13:52
22m
Talk
Fast, Multicore-Scalable, Low-Fragmentation Memory Allocation through Large Virtual Memory and Global Data StructuresOOPSLA Artifact
OOPSLA
Martin Aigner University of Salzburg, Austria, Christoph Kirsch University of Salzburg, Austria, Michael Lippautz University of Salzburg, Austria, Ana Sokolova University of Salzburg, Austria
DOI Pre-print Media Attached
14:15
22m
Talk
Probability Type Inference for Flexible Approximate Programming
OOPSLA
Brett Boston Massachusetts Institute of Technology, USA, Adrian Sampson Cornell University & Microsoft Research, Dan Grossman University of Washington, USA, Luis Ceze University of Washington, USA
Pre-print Media Attached
14:37
22m
Talk
Cross-Layer Memory Management for Managed Language Applications
OOPSLA
Michael Jantz University of Tennessee, USA, Forrest Robinson University of Kansas, USA, Prasad Kulkarni University of Kansas, Kshitij Doshi Intel, USA
DOI Media Attached