3/8/2023 0 Comments Deduplicator java app![]() Memory leaks cause systematic heap growth, degrading performance and resulting in program crashes after perhaps days or weeks of execution. Prior approaches for detecting memory leaks rely on heap differencing or detailed object statistics which store state proportional to the number of objects in the heap. These overheads preclude their use on the same processor for deployed long-running applications.This paper introduces a dynamic heap-summarization technique based on type that accurately identifies leaks, is space efficient (adding less than 1% to the heap), and is time efficient (adding 2.3% on average to total execution time). We implement this approach in Cork which utilizes dynamic type information and garbage collection to summarize the live objects in a type points-from graph (TPFG) whose nodes (types) and edges (references between types) are annotated with volume. ![]() Cork compares TPFGs across multiple collections, identifies growing data structures, and computes a type slice for the user. Cork is accurate: it identifies systematic heap growth with no false positives in 4 of 15 benchmarks we tested. Cork's slice report enabled us (non-experts) to quickly eliminate growing data structures in SPECjbb2000 and Elipse, something their developers had not previously done. Cork is accurate, scalable, and efficient enough to consider using online. Languages such as Java and C#, as well as scripting languages like Python, and Ruby, make extensive use of Collection classes. A collection implementation represents a fixed choice in the dimensions of operation time, space utilization, and synchronization. ![]() Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation. ![]() ![]() In this paper, we present CHAMELEON, a low-overhead automatic tool that assists the programmer in choosing the appropriate collection implementation for her application. During program execution, CHAMELEON computes elaborate trace and heap-based metrics on collection behavior. These metrics are consumed on-thefly by a rules engine which outputs a list of suggested collection adaptation strategies. The tool can apply these corrective strategies automatically or present them to the programmer. We have implemented CHAMELEON on top of a IBM's J9 production JVM, and evaluated it over a small set of benchmarks. ![]()
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