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

In approximate computing, programs gain efficiency by allowing occasional errors. Controlling the probabilistic effects of this approximation remains a key challenge. We propose a new approach where programmers use a type system to communicate high-level constraints on the degree of approximation. A combination of type inference, code specialization, and optional dynamic tracking makes the system expressive and convenient. The core type system captures the probability that each operation exhibits an error and bounds the probability that each expression deviates from its correct value. Solver-aided type inference lets the programmer specify the correctness probability on only some variables—program outputs, for example—and automatically fills in other types to meet these specifications. An optional dynamic type helps cope with complex run-time behavior where static approaches are insufficient. Together, these features interact to yield a high degree of programmer control while offering a strong soundness guarantee. We use existing approximate-computing benchmarks to show how our language, DECAF, maintains a low annotation burden. Our constraint-based approach can encode hardware details, such as finite degrees of reliability, so we also use DECAF to examine implications for approximate hardware design. We find that multi-level architectures can offer advantages over simpler two-level machines and that solver-aided optimization improves efficiency.

Thu 29 Oct
Times are displayed in time zone: Eastern Time (US & Canada) change

13:30 - 15:00: 7. RuntimeOOPSLA at Grand Station 1
Chair(s): Michael PradelTU Darmstadt, Germany
13:30 - 13:52
Accurate Profiling in the Presence of Dynamic CompilationOOPSLA Artifact
Yudi ZhengUniversity of Lugano, Lubomír BulejUniversità della Svizzera italiana, Walter BinderUniversity of Lugano
13:52 - 14:15
Fast, Multicore-Scalable, Low-Fragmentation Memory Allocation through Large Virtual Memory and Global Data StructuresOOPSLA Artifact
Martin AignerUniversity of Salzburg, Austria, Christoph KirschUniversity of Salzburg, Austria, Michael LippautzUniversity of Salzburg, Austria, Ana SokolovaUniversity of Salzburg, Austria
DOI Pre-print Media Attached
14:15 - 14:37
Probability Type Inference for Flexible Approximate Programming
Brett BostonMassachusetts Institute of Technology, USA, Adrian SampsonCornell University & Microsoft Research, Dan GrossmanUniversity of Washington, USA, Luis CezeUniversity of Washington, USA
Pre-print Media Attached
14:37 - 15:00
Cross-Layer Memory Management for Managed Language Applications
Michael JantzUniversity of Tennessee, USA, Forrest RobinsonUniversity of Kansas, USA, Prasad KulkarniUniversity of Kansas, Kshitij DoshiIntel, USA
DOI Media Attached