Dynamic memory allocation has been a fundamental part of most computer systems since roughly 1960, and memory allocation is widely considered to be either a solved problem or an insoluble one. In this survey, we describe a variety of memory allocator designs and point out issues relevant to their design and evaluation. We then chronologically survey most of the literature on allocators between 1961 and 1995. (Scores of papers are discussed, in varying detail, and over 150 references are given.) We argue that allocator designs have been unduly restricted by an emphasis on mechanism, rather than policy, while the latter is more important; higher-level strategic issues are still more important, but have not been given much attention. Most theoretical analyses and empirical allocator evaluations to date have relied on very strong assumptions of randomness and independence, but real program behavior exhibits important regularities that must be exploited if allocators are to perform well in practice.

Memory Management Units (MMUs) are traditionally used by operating systems to implement disk-paged virtual memory. Some operating systems allow user programs to specify the protection level (inaccessible, readonly. read-write) of pages, and allow user programs t.o handle protection violations. bur. these mechanisms are not. always robust, efficient, or well-mat. ched to the needs of applications.

. We have developed and implemented techniques that double the performance of dynamically-typed object-oriented languages. Our SELF implementation runs twice as fast as the fastest Smalltalk implementation, despite SELF's lack of classes and explicit variables. To compensate for the absence of classes, our system uses implementation-level maps to transparently group objects cloned from the same prototype, providing data type information and eliminating the apparent space overhead for prototype-based systems. To compensate for dynamic typing, user-defined control structures, and the lack of explicit variables, our system dynamically compiles multiple versions of a source method, each customized according to its receiver's map. Within each version the type of the receiver is fixed, and thus the compiler can statically bind and inline all messages sent to self. Message splitting and type prediction extract and preserve even more static type information, allowing the compiler to inline ma...

Languages that support abstraction and modular structure, such as Standard ML, Modula, Ada, and (more or less) C++, may have deeply nested dependency hierarchies among source files. In ML the problem is particularly severe because ML's powerful parameterized module (functor) facility entails dependencies among implementation modules, not just among interfaces.

Object-oriented programming languages promise to improve programmer productivity by supporting abstract data types, inheritance, and message passing directly within the language. Unfortunately, traditional implementations of object-oriented language features, particularly message passing, have been much slower than traditional implementations of their non-object-oriented counterparts: the fastest existing implementation of Smalltalk-80 runs at only a tenth the speed of an optimizing C implementation. The dearth of suitable implementation technology has forced most object-oriented languages to be designed as hybrids with traditional non-object-oriented languages, complicating the languages and making programs harder to extend and reuse. This dissertation describes a collection of implementation techniques that can improve the run-time performance of object-oriented languages, in hopes of reducing the need for hybrid languages and encouraging wider spread of purely object-oriented langu...

Abstract. Object-oriented languages have suffered from poor performance caused by frequent and slow dynamically-bound procedure calls. The best way to speed up a procedure call is to compile it out, but dynamic binding of object-oriented procedure calls without static receiver type information precludes inlining. Iterative type analysis and extended message splitting are new compilation techniques that extract much of the necessary type information and make it possible to hoist run-time type tests out of loops. Our system compiles code on-the-fly that is customized to the actual data types used by a running program. The compiler constructs a control flow graph annotated with type information by simultaneously performing type analysis and inlining. Extended message splitting preserves type information that would otherwise be lost by a control-flow merge by duplicating all the code between the merge and the place that uses the information. Iterative type analysis computes the types of variables used in a loop by repeatedly recompiling the loop until the computed types reach a fix-point. Together these two techniques enable our SELF compiler to split off a copy of an entire loop, optimized for the common-case types. By the time our SELF compiler generates code for the graph, it has eliminated many dynamicallydispatched

Abstract. We have developed and implemented techniques that double the performance of dynamically-typed object-oriented languages. Our SELF implementation runs twice as fast as the fastest Smalltalk implementation, despite SELF’s lack of classes and explicit variables. To compensate for the absence of classes, our system uses implementation-level maps to transparently group objects cloned from the same prototype, providing data type information and eliminating the apparent space overhead for prototype-based systems. To compensate for dynamic typing, user-defined control structures, and the lack of explicit variables, our system dynamically compiles multiple versions of a source method, each customized according to its receiver’s map. Within each version the type of the receiver is fixed, and thus the compiler can statically bind and inline all messages sent to self. Message splitting and type prediction extract and preserve even more static type information, allowing the compiler to inline many other messages. Inlining dramatically improves performance and eliminates the need to hard-wire low-level methods such as +, ==, and ifTrue:. Despite inlining and other optimizations, our system still supports interactive programming environments. The system traverses internal dependency lists to invalidate all compiled methods

Object-oriented programming languages confer many benefits, including abstraction, which lets the programmer hide the details of an object’s implementation from the object’s clients. Unfortunately, crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction, while desirable from a design standpoint, may be impractical when it leads to inefficient programs. Aggressive compiler optimizations can reduce the overhead of abstraction. However, the long compilation times introduced by optimizing compilers delay the programming environment‘s responses to changes in the program. Furthermore, optimization also conflicts with source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals by performing optimizations lazily. Four new techniques work together to achieve high performance and high responsiveness: • Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. On average, programs run 1.5 times faster than the previous SELF system; compared to a commercial Smalltalk implementation, two medium-sized benchmarks run about three times faster. This level of performance is obtained with a compiler that is both simpler and faster than previous SELF compilers. • Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast nonoptimizing compiler to generate initial code while automatically recompiling heavily used parts of the program with an optimizing compiler. On a previous-generation workstation like the SPARCstation-2, fewer than 200 pauses exceeded 200 ms during a 50-minute interaction, and 21 pauses exceeded one second. On a currentgeneration workstation, only 13 pauses exceed 400 ms. • Dynamic deoptimization shields the programmer from the complexity of debugging optimized code by transparently recreating non-optimized code as needed. No matter whether a program is optimized or not, it can always be stopped, inspected, and single-stepped. Compared to previous approaches, deoptimization allows more debugging while placing fewer restrictions on the optimizations that can be performed. • Polymorphic inline caching generates type-case sequences on-the-fly to speed up messages sent from the same call site to several different types of object. More significantly, they collect concrete type information for the optimizing compiler. With better performance yet good interactive behavior, these techniques make exploratory programming possible both for pure object-oriented languages and for application domains requiring higher ultimate performance, reconciling exploratory programming, ubiquitous abstraction, and high performance.

Standard ML is an excellent language for many kinds of programming. It is safe, efficient, suitably abstract, and concise. There are many aspects of the language that work well. However, nothing is perfect: Standard ML has a few shortcomings. In some cases there are obvious solutions, and in other cases further research is required.

We have designed and implemented a copying garbage-collection algorithm that is efficient, real-time, concurrent, runs on commerial uniprocessors and shared-memory multiprocessors, and requires no change to compilers. The algorithm uses standard virtual-memory hardware to detect references to "from space" objects and to synchronize the collector and mutator threads. We have implemented and measured a prototype running on SRC's 5-processor Firefly. It will be straightforward to merge our techniques with generational collection. An incremental, non-concurrent version could be implemented easily on many versions of Unix. Introduction This paper presents the first copying garbage-collection algorithm that is efficient, real-time, concurrent, runs on stock commercial uniprocessors and multiprocessors, and requires no change to compilers. A collection algorithm is efficient if the amortized cost to allocate, access, and collect an object is small compared to the cost of initializing the o...