NDLI: Go with the flow:profiling copies to find runtime bloat

Content Provider	ACM Digital Library
Author	Sevitsky, Gary Rountev, Atanas Arnold, Matthew Xu, Guoqing Mitchell, Nick
Abstract	Many large-scale Java applications suffer from runtime bloat. They execute large volumes of methods, and create many temporary objects, all to execute relatively simple operations. There are large opportunities for performance optimizations in these applications, but most are being missed by existing optimization and tooling technology. While JIT optimizations struggle for a few percent, performance experts analyze deployed applications and regularly find gains of 2x or more. Finding such big gains is difficult, for both humans and compilers, because of the diffuse nature of runtime bloat. Time is spread thinly across calling contexts, making it difficult to judge how to improve performance. Bloat results from a pile-up of seemingly harmless decisions. Each adds temporary objects and method calls, and often copies values between those temporary objects. While data copies are not the entirety of bloat, we have observed that they are excellent indicators of regions of excessive activity. By optimizing copies, one is likely to remove the objects that carry copied values, and the method calls that allocate and populate them. We introduce copy profiling, a technique that summarizes runtime activity in terms of chains of data copies. A flat copy profile counts copies by method. We show how flat profiles alone can be helpful. In many cases, diagnosing a problem requires data flow context. Tracking and making sense of raw copy chains does not scale, so we introduce a summarizing abstraction called the copy graph. We implement three clients analyses that, using the copy graph, expose common patterns of bloat, such as finding hot copy chains and discovering temporary data structures. We demonstrate, with examples from a large-scale commercial application and several benchmarks, that copy profiling can be used by a programmer to quickly find opportunities for large performance gains.
Starting Page	419
Ending Page	430
Page Count	12
File Format	PDF
ISSN	03621340 15581160
DOI	10.1145/1543135.1542523
Journal	ACM SIGPLAN Notices (SIGP)
Volume Number	44
Issue Number	6
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1983-05-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Profiling Copy graph Heap analysis Memory bloat
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Go with the flow: profiling copies to find runtime bloat

Efficiently and precisely locating memory leaks and bloat

Continuous object access profiling and optimizations to overcome the memory wall and bloat (SIGP, Vol. 47, Issue. 4)

Quantitative Interprocedural Analysis

Runtime specialization with optimistic heap analysis

Symbolic heap abstraction with demand-driven axiomatization of memory invariants

A bloat-aware design for big data applications

The causes of bloat, the limits of health

Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing

Go with the flow:profiling copies to find runtime bloat

Similar Documents

Go with the flow: profiling copies to find runtime bloat

Efficiently and precisely locating memory leaks and bloat

Continuous object access profiling and optimizations to overcome the memory wall and bloat (SIGP, Vol. 47, Issue. 4)

Quantitative Interprocedural Analysis

Runtime specialization with optimistic heap analysis

Symbolic heap abstraction with demand-driven axiomatization of memory invariants

A bloat-aware design for big data applications

The causes of bloat, the limits of health

Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing

Go with the flow:profiling copies to find runtime bloat