Tag-free garbage collection for strongly typed programming languages

1991 ◽  
Author(s):  
Benjamin Goldberg
Keyword(s):  
Programming Languages ◽  
Garbage Collection

scholarly journals Profiling Initialisation Behaviour in Java

2021 ◽  
Author(s):  
◽  
Stephen Frank Nelson
Keyword(s):  
Programming Languages ◽  
Program Analysis ◽  
Garbage Collection ◽  
Hybrid Approach ◽  
Memory Consumption ◽  
Aspect Oriented Programming ◽  
Analysis Study ◽  
Programming Tools ◽  
Mutable State ◽  
Blank Slate

<p>Freshly created objects are a blank slate: their mutable state and their constant properties must be initialised before they can be used. Programming languages like Java typically support object initialisation by providing constructor methods. This thesis examines the actual initialisation of objects in real-world programs to determine whether constructor methods support the initialisation that programmers actually perform. Determining which object initialisation techniques are most popular and how they can be identified will allow language designers to better understand the needs of programmers, and give insights that VM designers could use to optimise the performance of language implementations, reduce memory consumption, and improve garbage collection behaviour. Traditional profiling typically either focuses on timing, or uses sampling or heap snapshots to approximate whole program analysis. Classifying the behaviour of objects throughout their lifetime requires analysis of all program behaviour without approximation. This thesis presents two novel whole-program object profilers: one using purely class modification (#prof ), and a hybrid approach utilising class modification and JVM support (rprof ). #prof modifies programs using aspect-oriented programming tools to generate and aggregate data and examines objects that enter different collections to determine whether correlation exists between initialisation behaviour and the use of equality operators and collections. rprof confirms the results of an existing static analysis study of field initialisation using runtime analysis, and provides a novel study of object initialisation behaviour patterns.</p>

scholarly journals Systematic abstraction of abstract machines

2012 ◽  
Vol 22 (4-5) ◽  
pp. 705-746 ◽  
Author(s):  
DAVID VAN HORN ◽  
MATTHEW MIGHT
Keyword(s):  
Programming Languages ◽  
Static Analysis ◽  
Abstract Interpretation ◽  
Garbage Collection ◽  
Higher Order ◽  
Return Flow ◽  
Initial Development ◽  
Abstract Machines ◽  
Temporal Ordering

AbstractWe describe a derivational approach to abstract interpretation that yields novel and transparently sound static analyses when applied to well-established abstract machines for higher-order and imperative programming languages. To demonstrate the technique and support our claim, we transform the CEK machine of Felleisen and Friedman (Proc. of the 14th ACM SIGACT-SIGPLAN Symp. Prin. Program. Langs, 1987, pp. 314–325), a lazy variant of Krivine's machine (Higher-Order Symb. Comput. Vol 20, 2007, pp. 199–207), and the stack-inspecting CM machine of Clements and Felleisen (ACM Trans. Program. Lang. Syst. Vol 26, 2004, pp. 1029–1052) into abstract interpretations of themselves. The resulting analyses bound temporal ordering of program events; predict return-flow and stack-inspection behavior; and approximate the flow and evaluation of by-need parameters. For all of these machines, we find that a series of well-known concrete machine refactorings, plus a technique of store-allocated continuations, leads to machines that abstract into static analyses simply by bounding their stores. These machines are parameterized by allocation functions that tune performance and precision and substantially expand the space of analyses that this framework can represent. We demonstrate that the technique scales up uniformly to allow static analysis of realistic language features, including tail calls, conditionals, mutation, exceptions, first-class continuations, and even garbage collection. In order to close the gap between formalism and implementation, we provide translations of the mathematics as running Haskell code for the initial development of our method.

Tag-free garbage collection for strongly typed programming languages

1991 ◽  
Vol 26 (6) ◽  
pp. 165-176 ◽  
Author(s):  
Benjamin Goldberg
Keyword(s):  
Programming Languages ◽  
Garbage Collection

scholarly journals A performance comparison of garbage collector algorithms in Java Virtual Machine

2019 ◽  
Vol 13 ◽  
pp. 359-365
Author(s):  
Igor Kopeć ◽  
Jakub Smołka
Keyword(s):  
Programming Languages ◽  
Memory Management ◽  
Garbage Collection ◽  
Performance Comparison ◽  
Java Virtual Machine ◽  
Garbage Collector ◽  
System Responsiveness ◽  
Automatic Memory ◽  
A Performance

In programming languages with automatic memory management garbage collection plays an important role of cleaning unused memory. Garbage collection algorithms have been developed for many years and aim to maximize the application’s performance. This paper presents and compares a performance of five garbage collection algorithms present in current version of Java 12 in three applications with different object lifetime span. The analysis covered the system responsiveness, garbage collector workload and application throughput at high application load.

Integrated Hardware Garbage Collection

2021 ◽  
Vol 20 (5) ◽  
pp. 1-25
Author(s):  
Andrés Amaya García ◽  
David May ◽  
Ed Nutting
Keyword(s):  
Software Development ◽  
Programming Languages ◽  
Real Time ◽  
Program Analysis ◽  
Garbage Collection ◽  
Data Representation ◽  
Garbage Collector ◽  
Tightly Coupled ◽  
Memory Cycle ◽  
High Level

Garbage collected programming languages, such as Python and C#, have accelerated software development. These modern languages increase productivity and software reliability as they provide high-level data representation and control structures. Modern languages are widely used in software development for mobile, desktop, and server devices, but their adoption is limited in real-time embedded systems. There is clear interest in supporting modern languages in embedded devices as emerging markets, like the Internet of Things, demand ever smarter and more reliable products. Multiple commercial and open-source projects, such as Zerynth and MicroPython, are attempting to provide support. But these projects rely on software garbage collectors that impose high overheads and introduce unpredictable pauses, preventing their use in many embedded applications. These limitations arise from the unsuitability of conventional processors for performing efficient, predictable garbage collection. We propose the Integrated Hardware Garbage Collector (IHGC); a garbage collector tightly coupled with the processor that runs continuously in the background. Further, we introduce a static analysis technique to guarantee that real-time programs are never paused by the collector. Our design allocates a memory cycle to the collector when the processor is not using the memory. The IHGC achieves this by careful division of collection work into single-memory-access steps that are interleaved with the processor’s memory accesses. As a result, our collector eliminates run-time overheads and enables real-time program analysis. The principles behind the IHGC can be used in conjunction with existing architectures. For example, we simulated the IHGC alongside the ARMv6-M architecture. Compared to a conventional processor, our experiments indicate that the IHGC offers 1.5–7 times better performance for programs that rely on garbage collection. The IHGC delivers the benefits of garbage-collected languages with real-time performance but without the complexity and overheads inherent in software collectors.

scholarly journals Profiling Initialisation Behaviour in Java

2021 ◽  
Author(s):  
◽  
Stephen Frank Nelson
Keyword(s):  
Programming Languages ◽  
Program Analysis ◽  
Garbage Collection ◽  
Hybrid Approach ◽  
Memory Consumption ◽  
Aspect Oriented Programming ◽  
Analysis Study ◽  
Programming Tools ◽  
Mutable State ◽  
Blank Slate

<p>Freshly created objects are a blank slate: their mutable state and their constant properties must be initialised before they can be used. Programming languages like Java typically support object initialisation by providing constructor methods. This thesis examines the actual initialisation of objects in real-world programs to determine whether constructor methods support the initialisation that programmers actually perform. Determining which object initialisation techniques are most popular and how they can be identified will allow language designers to better understand the needs of programmers, and give insights that VM designers could use to optimise the performance of language implementations, reduce memory consumption, and improve garbage collection behaviour. Traditional profiling typically either focuses on timing, or uses sampling or heap snapshots to approximate whole program analysis. Classifying the behaviour of objects throughout their lifetime requires analysis of all program behaviour without approximation. This thesis presents two novel whole-program object profilers: one using purely class modification (#prof ), and a hybrid approach utilising class modification and JVM support (rprof ). #prof modifies programs using aspect-oriented programming tools to generate and aggregate data and examines objects that enter different collections to determine whether correlation exists between initialisation behaviour and the use of equality operators and collections. rprof confirms the results of an existing static analysis study of field initialisation using runtime analysis, and provides a novel study of object initialisation behaviour patterns.</p>

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