John Reppy's Publications

Standard ML

Compiling with continuations and LLVM.
Kavon Farvardin and John Reppy. In Proceedings of the 2016 ACM SIGPLAN Workshop on ML, September 2016.
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This paper describes a new LLVM-based backend for the Parallel ML compiler (part of the Manticore system). This backend is novel in that it supports heap-allocated first-class continuations (a first for LLVM), which, in turn enables language features, such as callcc, lightweight concurrency mechanisms, and PML's parallelism features.

SML3d: 3D Graphics for Standard ML.
John Reppy. In Proceedings of the 2014 ACM SIGPLAN Workshop on ML, September 2014.
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The SML3d system is a collection of libraries designed to support real-time 3D graphics programming in Standard ML (SML). This paper gives an overview of the system and briefly highlights some of the more interesting aspects of its design and implementation.

A Declarative API for Particle Systems.
Pavel Krajcevski and John Reppy. In Proceedings of the Thirteenth International Symposium on Practical Aspects of Declarative Languages (PADL 2011), Volume 6539 of Lecture Notes in Computer Science, pages 130--144, New York, NY, January 2011. Springer-Verlag.
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Recent trends in computer-graphics APIs and hardware have made it practical to use high-level functional languages for real-time graphics applications. Thus we have the opportunity to develop new approaches to computer graphics that take advantage of the high-level features of functional languages. This paper describes one such project that uses the techniques of functional programming to define and implement a combinator library for particle systems. Particle systems are a popular technique for rendering fuzzy phenomena, such as fire, smoke, and explosions. Using our combinators, a programmer can provide a declarative specification of how a particle system behaves. This specification includes rules for how particles are created, how they evolve, and how they are rendered. Our library translates these declarative specifications into a low-level intermediate language that can be compiled to run on the GPU or interpreted by the CPU.

Calling Variadic Functions from a Strongly-typed Language.
Matthias Blume, Mike Rainey, and John Reppy. In Proceedings of the 2008 ACM SIGPLAN Workshop on ML, pages 47--58, September 2008.
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The importance of providing a mechanism to call C functions from high-level languages has been understood for many years and, these days, almost all statically-typed high-level-language implementations provide such a mechanism. One glaring omission, however, has been support for calling variadic C functions, such as printf. Variadic functions have been ignored because it is not obvious how to give static types to them and because it is not clear how to generate calling sequence when the arguments to the function may not be known until runtime. In this paper, we address this longstanding omission with an extension to the NLFFI foreign-interface framework used by Standard ML of New Jersey (SML/NJ) and the MLton SML compiler. We describe two different ways of typing variadic functions in NLFFI and an implementation technique based on the idea of using state machines to describe calling conventions. Our implementation is easily retargeted to new architectures and ABIs, and can also be easily added to any HOT language implementation that supports calling C functions.

Type-sensitive control-flow analysis.
John Reppy. In Proceedings of the 2006 ACM SIGPLAN Workshop on ML, pages 74--83, September 2006.
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Higher-order typed languages, such as ML, provide strong support for data and type abstraction. While such abstraction is often viewed as costing performance, there are situations where it may provide opportunities for more aggressive program optimization. Specifically, we can exploit the fact that type abstraction guarantees representation independence, which allows the compiler to specialize data representations. This paper describes a first step in supporting such optimizations; namely a control-flow analysis that uses the program's type information to compute more precise results. We present our algorithm as an extension of Serrano's version of 0-CFA and we show that it respects types. We also discuss applications of the analysis with examples of optimizations enabled by the analysis that would not be possible normal CFA.

The Standard ML Basis Library.
Emden R. Gansner and John H. Reppy, editors. Cambridge University Press, 2004.
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Concurrent Programming in ML.
John H. Reppy. Cambridge University Press, Cambridge, England, 1999.
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AML: Attribute Grammars in ML.
S.G. Efremidis, K.A. Mughal, L. Søraas, and John Reppy. Nordic Journal of Computing, 4(1), 1997.
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Unrolling lists.
Zhong Shao, John Reppy, and Andrew Appel. In ACM Conference on Lisp and Functional Programming, pages 185--195, June 1994.
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A portable and optimizing back end for the SML/NJ compiler.
Lal George, Florent Guillame, and John Reppy. In Fifth International Conference on Compiler Construction, pages 83--97, April 1994.
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A High-performance Garbage Collector for Standard ML.
John H. Reppy. Technical memo, AT&T Bell Laboratories, December 1993.
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Abstract Value Constructors: Symbolic Constants for Standard ML.
William E. Aitken and John H. Reppy. Technical Report TR 92-1290, Department of Computer Science, Cornell University, June 1992. A shorter version appears in the proceedings of the “ACM SIGPLAN Workshop on ML and its Applications,” 1992.
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Standard ML (SML) has been used to implement a wide variety of large systems, such as compilers, theorem provers, graphics libraries, and even operating systems. While SML provides a convenient, high-level notation for programming large applications, it does have certain deficiencies. One such deficiency is the lack of a general mechanism for assigning symbolic names to constant values. In this paper, we present a simple extension of SML that corrects this deficiency in a way that fits naturally with the semantics of SML. Our proposal is a generalization of SML's datatype constructors: we introduce constants that generalize nullary datatype constructors (like nil), and templates that generalize non-nullary datatype constructors (like ::). Constants are identifiers bound to fixed values, and templates are identifiers bound to structured values with labeled holes. Templates are useful because they allow users to treat the representation of structured data abstractly without having to give up pattern matching.

Attribute grammars in ML.
S.G. Efremidis, K.A. Mughal, and John Reppy. In ACM SIGPLAN Workshop on ML and its Aplications, June 1992.
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Higher-order Concurrency.
John H. Reppy. PhD thesis, Cornell University, 1992. Available as Computer Science Technical Report 92-1285.
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CML: A higher-order concurrent language.
John H. Reppy. In Proceedings of the SIGPLAN 1991 Conference on Programming Language Design and Implementation, pages 293--305, New York, NY, June 1991. ACM.
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Asynchronous signals in Standard ML.
John H. Reppy. Technical Report TR 90-1144, Department of Computer Science, Cornell University, Ithaca, NY, August 1990.
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Last updated on August 31, 2018
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