M. Cierniak, G.-Y. Lueh, and J. M. Stichnoth. Practicing JUDO: Java under dynamic optimizations. In SIGPLAN Conference on Programming Language Design and Implementation, pages 13-26, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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Online Feedback-Directed Optimization of Java - Arnold, Hind, Ryder (2002) (3 citations) (Correct)
....optimizing compiler to improve application performance. Because such optimization is performed at runtime, attention has focused on limiting the overhead of runtime optimization by either reducing the execution time of the optimizer or applying optimization only to key portions of the application [30, 39, 43, 21, 6]. The latter selective optimization approach requires a careful tradeo# between the benefit of reduced optimization time and the increased runtime cost of detecting these key portions. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted ....
....patching to profile values of method parameters. This idea is extended in [44] to capture dynamic call graph information to drive inlining. The results in these papers do not report the performance while executing the instrumentation; only the final steady state performance is given. The MRL VM [21] uses a two stage compile only approach. Recompilation is triggered using two mechanisms. The first mechanism, similar to [30, 39, 43] inserts counters in the non optimized code that track method invocations and the execution of loop back edges. These counters are initialized to 1,000 and 10,000, ....
Michal Cierniak, Guei-Yuan Lueh, and James M. Stichnoth. Practicing JUDO: Java Under Dynamic Optimizations. In SIGPLAN 2000.
Online Profiling And Feedback-Directed Optimization Of Java - Arnold (2002) (1 citation) (Correct)
....static compilation, most notably that the overhead incurred by performing compilation at runtime can be substantial. To minimize this overhead, attention has been focused on 1) reducing the execution time of the optimizer, and 2) applying optimization to only the key portions of the application [51, 64, 71, 36, 8]. This second approach, often referred to as selective optimization, avoids the overhead of optimizing all methods, thus is particularly beneficial for shorter running programs that do not execute long enough to recoup the time spent optimizing all methods [11] Despite its potential ....
.... 26, 4] for collecting the kinds of profiles often used to drive feedbackdirected optimizations, and overheads in the range of 10,000 (100 times slower) have been reported [26] This overhead is one of the main reasons why today s JVM s perform only limited forms of feedback directed optimizations [8, 71, 36, 64]. Optimizations that are currently being used online are usually based on profiles that can be collected easily with low overhead. Some online systems, such as Dynamo [16] are designed to identify when performance is being degraded so that profile guided optimizations can be disabled for the ....
[Article contains additional citation context not shown here]
Michal Cierniak, Guei-Yuan Lueh, and James M. Stichnoth. Practicing JUDO: Java Under Dynamic Optimizations. In SIGPLAN 2000.
Detecting Malicious Java Code Using Virtual Machine Auditing - Soman, Krintz, Vigna (2003) (1 citation) (Correct)
....protection against some programming errors and malicious attacks. The key areas that must be improved to enable continued wide spread use of Java for Internet scale server applications are performance and security. Recent advances in Just In Time (JIT) compilation and optimization techniques [35, 6, 8] offer significant improvements in Java program performance. Currently, to support security in server applications, Java provides mechanisms for authentication and access control [17, 2] However, additional mechanisms are required to detect attacks that circumvent (or attempt to circumvent) the ....
M. Cierniak, G. Lueh, and J. Stichnoth. Practicing JUDO: Java Under Dynamic Optimizations. In Proceedings of the ACM SIGPLAN 2000.
Automating Selective Dynamic Compilation - Mock (2002) (Correct)
....remains to be seen whether the Replay architecture would be able to produce significant speedups in practice. 26 2.4 Adaptive Run time Compilation with JITs Java [GJS96] has become the de facto standard programming language for Web Page designers. This has resulted in a lot of research [CL97, CLS00, ATCL 98, YMP 99, AFG 00] aimed at improving the performance of implementations of the Java Virtual Machine (JVMs) which is a platformindependent mechanism for executing Java programs distributed in the Java bytecode format the preferred way of providing functionality to users ....
....cost, JVMs typically use an adaptive optimization mechanism, whose goal is to identify Java methods that warrant higher compilation cost because of their high execution frequencies. Consequently, IBM s Jalape no [AFG 00] the Intel Microprocessor Research Lab Virtual Machine (MRL JVM) CLS00] and Sun s Hotspot [hot99] include an adaptive optimization mechanism that selectively (re ) compiles Java methods to improve program performance. The work of Arnold et al. AFG 00] on Jalape no is closest to the automatic optimization approach taken in this dissertation, since they use an ....
Michal Cierniak, Guei-Yuan Lueh, and James M. Stichnoth. Practicing JUDO: Java under dynamic optimizations. In Proceedings of the ACM SIGPLAN '00 Conference on Programming Language Design and Implementation, pages 13--26, Vancouver, BC, Canada, June 2000.
Partial Method Compilation using Dynamic Profile Information - Whaley (2001) (11 citations) (Correct)
.... interpreting the code, and when the execution count for the method reaches a certain threshold or by some other heuristic, they use the optimizing compiler to dynamically compile the code for the method [42, 38] Some systems use a fast code generator (baseline compiler) rather than an interpreter [15, 9]. The problem with these systems is that the execution speed of the interpreted or baseline compiled code is signi cantly worse than that of fully optimized code typically 30 to ten times slower for baseline compiled code [15, 9] and ten to a hundred times slower for interpreted code [42, ....
.... code generator (baseline compiler) rather than an interpreter [15, 9] The problem with these systems is that the execution speed of the interpreted or baseline compiled code is signi cantly worse than that of fully optimized code typically 30 to ten times slower for baseline compiled code [15, 9] and ten to a hundred times slower for interpreted code [42, 38] Therefore, we would like to transfer into the optimized version as quickly as possible. However, the optimizing compiler can take a long time to compile. Waiting for the optimizing compiler to nish hurts program startup and ....
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M. Cierniak, G.-Y. Lueh, and J. M. Stichnoth. Practicing JUDO: Java under dynamic optimizations. In SIGPLAN Conference on Programming Language Design and Implementation, pages 13-26, 2000.
The Open Runtime Platform: A Flexible High-Performance Managed .. - Cierniak, al. (2003) (3 citations) Self-citation (Cierniak Stichnoth) (Correct)
....Runtime Platform (ORP) ORP was designed to support experimentation with different technologies in just in time compilers (JITs) garbage collection (GC) multithreading, and synchronization. Over the past five years, researchers have used ORP to conduct a number of MRTE implementation experiments [15 17, 19 21, 23, 25]. At least three different garbage collectors and eight different JITs have been developed and integrated with ORP. The version of ORP described in this paper is an internal research tool and is not publicly available. Three characteristics of MRTEs provide the key challenges to their ....
....a registered class hierarchy assumption, it calls back into the JIT that registered the assumption, to instruct it to deoptimize the code to use the standard dispatch mechanism for virtual methods. This is a variant of guarded devirtualization and does not require stack frame patching (see [17] for more details) The following functions in the JIT interface are used in this scheme: method is overridden(Method Handle m) This function checks if the method has been overridden in any of the subclasses. method set inline assumption(Method Handle caller, Method Handle callee) This ....
Cierniak, M., Lueh, G.-Y. and Stichnoth, J.M., "Practicing JUDO: Java Under Dynamic Optimizations," ACM Conference on Programming Language Design and Implementation, Vancouver, British Columbia, 2000.
Partial Method Compilation using Dynamic Profile Information - Whaley (2001) (11 citations) (Correct)
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M. Cierniak, G.-Y. Lueh, and J. M. Stichnoth. Practicing JUDO: Java under dynamic optimizations. In SIGPLAN Conference on Programming Language Design and Implementation, pages 13-26, 2000.
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