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  • Memory Efficient Hard Real-Time Garbage Collection by Tobias Ritzau PDF 下载

    Memory Efficient Hard Real-Time Garbage Collection by Tobias Ritzau  PDF 下载

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    Introduction
    This thesis presents work in the area of automatic memory management
    for hard real-time and embedded systems. The motivation of the thesis is
    to be able to develop hard real-time and embedded systems using modern languages. Since these languages commonly use automatic memory
    management or garbage collection (GC), which traditionally has had an
    unpredictable runtime behavior, we could either try to eliminate the need
    for GC using manual techniques, or we could develop GC techniques for
    these systems. Since GC is such a powerful tool to eliminate memory related programming errors, we decided to develop techniques to use GC in
    hard real-time and embedded systems. During this work three other GC
    techniques for these systems have been published. The main advantage
    of our work compared to the other three is that memory utilization effi-
    ciency increased by about 50 %. We have also developed an optimization
    for incremental garbage collectors and a static garbage collector that aims
    to eliminate the need for runtime garbage collection.
    1.1 Perspective
    Once upon a time, programming required a deep knowledge of how the
    machines were constructed and the programmers had full control of the
    execution of the system. Charles Babbage became the first programmer
    when he programmed his difference machine in 1822. It was programmed
    by exchanging the gears that performed the calculations. More than 100
    years later in about 1945 Konrad Zuse developed Plankalk¨ul [BW72], the
    first programming language. Unfortunately, most work was lost or con-
    fiscated in the aftermath of World War II and the work was not published
    until 1972. Plankalk¨ul was used to program the Z3, the first universal computer in the world [Roj98].
    2 CHAPTER 1. INTRODUCTION
    Contemporary computers were more like calculators, and the calculations where input by punching holes in paper tapes (Z3 and Colossus) or
    even by making physical changes to the hardware (ENIAC). In 1945 John
    von Neumann published the EDVAC report [vN45] and Alan Turing published the ACE Report [TCD86]. Both came to the conclusion that programs should be stored in memory in the same way as data was. This
    was the birth of the computer architecture that is still used today. In 1949,
    Short Code [Sch88] was introduced by John W. Mauchly, it was the first
    programming languages for the new generation computers.
    Programming languages has since evolved, adding features like recursion, pointers, dynamic memory management, garbage collection, structured programming, object-orientation, etc. Many of these features have
    become natural parts of programming languages, and most developers
    can not write a non-trivial program without them. These features makes
    programming less error-prone, and more complex systems can be implemented. However, with a higher level of abstraction, the control of the
    applications runtime behavior is lost. When developing real-time systems,
    i.e. systems whose correctness is not only dependent of their output but
    also on their timing, it is crucial that the runtime behavior can be predicted.
    A conflict occurs when real-time systems become increasingly more
    complex. Modern languages would certainly ease development and produce more stable systems, but the control of the runtime behavior is lost.
    The features of modern languages are not the problem, it is the way they
    are implemented that cause problems. Their implementations usually try
    to optimize average performance, and not worst case performance as is
    required in real-time systems. This thesis focuses on automatic memory
    management of real-time and embedded systems, and presents techniques
    to make it predictable and still efficient.
    1.2 Problem Definition
    To be able to maintain full control of the runtime behavior of a system,
    it must be possible to predict the amount of resources (e.g. CPU time and
    memory) that is required for any (virtual) machine level instruction and for
    all runtime system work. Note that using such a system does not prevent
    writing an unpredictable application. An example is an application that
    waits for external events, e.g. input from a user. First, it is not always
    possible to know when the event occurs, and second the data passed with
    the event may be unknown. Thus, developers must still follow rules to
    handle such cases.
    Early implementations of new languages are typically designed to be
    easy to implement and prove correct. Then follows optimizations for the
    average case, which is commonly interactive window based applications
    1.3. CONTRIBUTIONS 3
    or possibly servers. Techniques that are optimized for such systems are seldom appropriate for hard real-time and embedded systems, because their
    target systems need not be predictable and they have much more memory
    resources available.
    To be more specific, garbage collection algorithms may be designed to
    interrupt the application for short time periods in the general case, but it
    need not be guaranteed that it will collect all garbage memory before the
    system runs out of memory. If the memory runs out, the system can be
    stopped to collect the remaining garbage memory. Such stop may take
    a second or two, but that does not matter to these systems. Unfortunately
    many such techniques are called real-time garbage collectors, which is confusing. Another problem with garbage collectors is that they consume very
    much memory. The runtime systems that use real-time garbage collectors
    that guarantee memory availability need about 70 % of the system memory for internal use, which leaves about 30 % for the application. A large
    contribution to the overhead comes from the memory that is needed to allocate objects while the garbage collector collects garbage memory. This
    alone typically causes an overhead of about 50 %.
    The garbage collector is not the only part of a runtime system that needs
    to be redesigned to make it predictable. Examples of other parts that need
    attention are thread support, synchronization, messaging, and some complex instructions. This is, however, out of scope for this thesis.

     

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