Escape analysis on lists

Park, Young Gil; Goldberg, Benjamin

doi:10.1145/143103.143125

“…Guyer et al [4] identify dead objects by combining a lightweight pointer analysis with liveness information that detects the time point when short-lived objects die, then insert calls to free function. They conduct experiments by hybridizing their approach with mark-sweep GC and generational GC.…”

Section: Related Workmentioning

confidence: 99%

Cycler: Improve Heap Management for Allocation-Intensive Applications with On-the-Fly Object Reuse

Xiao¹,

Li²

2012

Parallel and Distributed Computing and Systems

0

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Tracing garbage collection (GC) identifies dead objects by scanning the object-graph from the root set references. It is proven to be highly efficient and widely used in commercial managed runtime systems for Java, C# and scripting languages. However, tracing GC cannot identify the dead objects before finishing object-graph traversal. Hence the application cannot reuse the space of the dead objects in realtime when the objects are no longer live. Even there are lots of dead objects, the system has to keep allocating new space for new objects until the object-graph is scanned, which has negative impact on both space efficiency, bandwidth efficiency and cache efficiency. The issue becomes serious with allocation-intensive applications. Some prior work tries to improve tracing GC with compilation analysis to identify the dead objects in compilation time. But they can only work with sub-optimal GC algorithm such as mark-sweep, and/or can only discover a small portion of the dead objects. In this paper we propose an algorithm Cycler that can reuse the dead objects in real-time before the object graph scanning. Cycler enhances the existing high-performance tracing GC with reference counting. The novelty of the algorithm is that it does not interfere with the existing optimal tracing GC, so it can effectively improve the overall performance, as long as the runtime overhead of reference counting is well constrained. To control the runtime overhead, Cycler only counts the object references of major types. In this paper, we describe Cycler design and implementation, and also present our evaluations with standard benchmarks.

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“…Compilation-time analysis like liveness analysis or escape analysis [4] tries to identify the objects' live ranges. Reference-counting and tracing analysis are runtime analyses [5].…”

Section: Related Workmentioning

confidence: 99%

Cycler: Improve Heap Management for Allocation-Intensive Applications with On-the-Fly Object Reuse

Li-juan¹,

Li²

2011

Parallel and Distributed Computing and Systems

0

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Tracing garbage collection (GC) identifies dead objects by scanning the object-graph from the root set references. It is proven to be highly efficient and widely used in commercial managed runtime systems for Java, C# and scripting languages. However, tracing GC cannot identify the dead objects before finishing object-graph traversal. Hence the application cannot reuse the space of the dead objects in realtime when the objects are no longer live. Even there are lots of dead objects, the system has to keep allocating new space for new objects until the object-graph is scanned, which has negative impact on both space efficiency, bandwidth efficiency and cache efficiency. The issue becomes serious with allocation-intensive applications. Some prior work tries to improve tracing GC with compilation analysis to identify the dead objects in compilation time. But they can only work with sub-optimal GC algorithm such as mark-sweep, and/or can only discover a small portion of the dead objects. In this paper we propose an algorithm Cycler that can reuse the dead objects in real-time before the object graph scanning. Cycler enhances the existing high-performance tracing GC with reference counting. The novelty of the algorithm is that it does not interfere with the existing optimal tracing GC, so it can effectively improve the overall performance, as long as the runtime overhead of reference counting is well constrained. To control the runtime overhead, Cycler only counts the object references of major types. In this paper, we describe Cycler design and implementation, and also present our evaluations with standard benchmarks.

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“…We use the notation A \ B for the difference on sets. The set of collectable objects can be approximated from the information computed by escape analysis [15,4]. The goal of escape analysis is to determine all program points where an object is reachable and whether the lifetime of the object can be proven to be restricted only to the current method.…”

Section: Inference Of Escape Informationmentioning

confidence: 99%

Live heap space analysis for languages with garbage collection

Albert

¹

,

Genaim

²

,

Gil

³

2009

Proceedings of the 2009 International Symposium on Memory Management

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The peak heap consumption of a program is the maximum size of the live data on the heap during the execution of the program, i.e., the minimum amount of heap space needed to run the program without exhausting the memory. It is well-known that garbage collection (GC) makes the problem of predicting the memory required to run a program difficult. This paper presents, the best of our knowledge, the first live heap space analysis for garbage-collected languages which infers accurate upper bounds on the peak heap usage of a program's execution that are not restricted to any complexity class, i.e., we can infer exponential, logarithmic, polynomial, etc., bounds. Our analysis is developed for an (sequential) object-oriented bytecode language with a scoped-memory manager that reclaims unreachable memory when methods return. We also show how our analysis can accommodate other GC schemes which are closer to the ideal GC which collects objects as soon as they become unreachable. The practicality of our approach is experimentally evaluated on a prototype implementation. We demonstrate that it is fully automatic, reasonably accurate and efficient by inferring live heap space bounds for a standardized set of benchmarks, the JOlden suite.

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Escape analysis on lists

Cited by 23 publications

References 15 publications

Cycler: Improve Heap Management for Allocation-Intensive Applications with On-the-Fly Object Reuse

Cycler: Improve Heap Management for Allocation-Intensive Applications with On-the-Fly Object Reuse

Cycler: Improve Heap Management for Allocation-Intensive Applications with On-the-Fly Object Reuse

Live heap space analysis for languages with garbage collection

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