Beltway: Getting Around Garbage Collection Gridlock

We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40%, and on average by 5 to 10%, for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors

Model Complexity of Program Phases

In resource limited computing systems, sequence prediction models must operate under tight constraints. Various models are available that cater to prediction under these conditions that in some way focus on reducing the cost of implementation. These resource constrained sequence prediction models, in practice, exhibit a fundamental tradeoff between the cost of implementation and the quality of its predictions. This fundamental tradeoff seems to be largely unexplored for models for different tasks. Here we formulate the necessary theory and an associated empirical procedure to explore this tradeoff space for a particular family of machine learning models such as deep neural networks. We anticipate that the knowledge of the behavior of this tradeoff may be beneficial in understanding the theoretical and practical limits of creation and deployment of models for resource constrained tasks

Pretenuring for Java

Pretenuring is a technique for reducing copying costs in garbage collectors. When pretenuring, the allocator places long-lived objects into regions that the garbage collector will rarely, if ever, collect. We extend previous work on profiling-driven pretenuring as follows. (1) We develop a collector-neutral approach to obtaining object lifetime profile information. We show that our collection of Java programs exhibits a very high degree of homogeneity of object lifetimes at each allocation site. This result is robust with respect to different inputs, and is similar to previous work on ML, but is in contrast to C programs, which require dynamic call chain context information to extract homogeneous lifetimes. Call-site homogeneity considerably simplifies the implementation of pretenuring and makes it more efficient. (2) Our pretenuring advice is neutral with respect to the collector algorithm, and we use it to improve two quite different garbage collectors: a traditional generational collector and an older-first collector. The system is also novel because it classifies and allocates objects into 3 categories: we allocate immortal objects into a permanent region that the collector will never consider, long-lived objects into a region in which the collector placed survivors of the most recent collection, and shortlived objects into the nursery, i.e., the default region. (3) We evaluate pretenuring on Java programs. Our simulation results show that pretenuring significantly reduces collector copying for generational and older-first collectors. 1

Parents dealing with anorexia : actions and meanings

This paper examines parents&rsquo; actions in response to anorexia nervosa, and how these are shaped by the ways they construct or understand the eating disorder. The findings indicate that parents try to influence their daughters by searching for help, providing practical support, avoiding confrontation, complying with special requirements, persuading, explaining, and pressuring, using ploys and force, providing emotional support, and mediating interactions. Parents&rsquo; actions are influenced by how they construct anorexia, such as whether they see it as an eating issue, an illness, a psychological problem, a choice, or a mystery. Understanding parents&rsquo; actions and constructions can help clinicians develop collaborative partnerships with parents.<br /

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Type-Based Alias Analysis

This paper evaluates three alias analyses based on programming language types. The first analysis uses type compatibility to determine aliases. The second extends the first by using additional high-level information such as field names. The third extends the second with a flow-insensitive analysis. Although other researchers suggests using types to disambiguate memory references, none evaluates its effectiveness. We perform both static and dynamic evaluations of type-based alias analyses for Modula-3, a statically-typed type-safe language. The static analysis reveals that type compatibility alone yields a very imprecise alias analysis, but the other two analyses significantly improve alias precision. We use redundant load elimination (RLE) to demonstrate the effectiveness of the three alias algorithms in terms of the opportunities for optimization, the impact on simulated execution times, and to compute an upper bound on what a perfect alias analysis would yield. We show modest dynamic improvements for (RLE), and more surprisingly, that on average our alias analysis is within 2.5% of a perfect alias analysis with respect to RLE on 8 Modula-3 programs. These results illustrate that to explore thoroughly the effectiveness of alias analyses, researchers need static, dynamic, and upper-bound analysis. In addition, we show that for type-safe languages like Modula-3 and Java, a fast and simple alias analysis may be sufficient for many applications