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The Many Facets of Operator Polymorphism in Julia

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The Many Facets of Operator Polymorphism in Julia The many facets of operator polymorphism in Julia Jiahao Chen Alan Edelman Massachusetts Institute of Technology Massachusetts Institute of Technology Computer Science and Artificial Intelligence Department of Mathematics, and Computer Laboratory Science and Artificial Intelligence Laboratory 77 Massachusetts Avenue 77 Massachusetts Avenue Cambridge, Massachusetts 02139, USA Cambridge, Massachusetts 02139, USA [email protected] [email protected] ABSTRACT parallel programming, where performance is a key consid- Polymorphism in programming languages enables code reuse. eration, exhibit a general dearth of abstraction and suffer Here, we show that polymorphism has broad applicability the consequent complexities in large, inscrutable and nearly far beyond computations for technical computing: not only unmaintainable codebases. Such challenges are particularly can the exact same code can be reused for serial and dis- acute for parallel programs, which are written primarily for tributed computing, but code can also be instrumented for performance reasons. However, parallel programs are noto- visualizing its data flow, and also be used for formal verifi- riously prone to subtle, nondeterministic bugs arising from cation of correctness. The ability to reuse a single codebase race conditions, and they are difficult to verify for correct- for all these purposes provides new ways to understand and ness. verify parallel programs. Programs for technical computing tend to sacrifice ab- straction for performance, but pay the cost in expressive- ness. In contrast, the mathematical structure of technical Categories and Subject Descriptors computations is inherently amenable to abstraction, particu- D.1.3 [Concurrent programming]: Distributed program- larly polymorphism. Just think of the myriad ways different ming; D.3.2 [Programming languages]: Very high-level mathematical objects can be multiplied together: one can languages; G.1.0 [General numerical analysis]: Parallel multiply two numbers together, or two matrices together, or algorithms a number and a matrix. The notion of multiplication itself can also be extended to dot products, cross products, tensor products, wedge products, outer products, and smash prod- General Terms ucts, to just name a few. All these operators share a com- Algorithms mon metaphor and it is natural to implement these products with polymorphic code that can express the mathematical commonality. Keywords In this paper, we argue that high level languages like Ju- Julia, prefix sum, scan, parallel prefix, polymorphism, in- lia provide a suitable framework for expressing high level strumentation, visualization abstractions that allow for extremely powerful code reuse, while affording a reasonable level of performance. In par- ticular, we explore how Julia's generic function system and 1. INTRODUCTION type system are suitable for expressing polymorphism at the Abstractions are the lifeblood of computer science [2]. operator level, and show how polymorphism can be used not Separating higher level concepts from lower level implemen- just to encompass different kinds of computations, but can tation details allows code to be more composable and reusable, be applied to parallelize code, enable visualizations of code and code using appropriate abstractions is simpler to under- implementations, and even verify correctness of code. stand and maintain. However, the desire for high level ab- stractions often seems inimical to writing high-performance 1.1 Multimethods in Julia code; oftentimes performance can only result from code that In this paper, we use the Julia language1, a very high takes advantage of low-level implementation details and hence level dynamic language designed specifically for technical break abstractions. Thus areas like technical computing and computing [5]. Julia offers language constructs that support different mechanisms of polymorphism, which programming language theorists call universal polymorphism and ad hoc polymorphism, or overloading [47]. In this paper, we focus Permission to make digital or hard copies of all or part of this work for only on Julia's overloading mechanism provided by multi- personal or classroom use is granted without fee provided that copies are methods. not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to Ad hoc polymorphism, or overloading, is a language con- republish, to post on servers or to redistribute to lists, requires prior specific 1 permission and/or a fee. Julia is MIT-licensed open source software and can be HPTCDL ’14 New Orleans, Louisiana USA downloaded freely from julialang.org. We use v0.3.1 in Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$15.00. this paper. struct that naturally expresses the polymorphism inherent Application Operator in the mathematical structure of technical computing [4]. Addition Consider the * operator which represents multiplication: the Poisson random variates [33] sequence lengths product can be taken between two integers, two floating- Minimal coverings [38] joining 2D regions point numbers, a scalar and a vector, or a matrix and a Stream reduction [24] counting records matrix, just to list a few typical possibilities. All these dif- Maximization ferent semantics can be represented by with the same syntax, Line of sight [7] height namely an expression of the form a*b. All programming lan- String alignment [22, 14] substring length guages resolve the ambiguity by considering the types of the Multiplication arguments a and b, which is formally equivalent to specify- Binary addition [44] Boolean matrices ing the domains of the operands [41]. Polynomial interpolation [17] scalars In practice, programming languages vary greatly in how Sorting [22, 6] permutations they allow users to reason about types. Some languages, like Tridiagonal equations [34] matrices Julia, offer the ability to define multimethods, where a single Function composition generic function like * can be defined with at more than one Finite state automata [32, 22] transition functions method, each with a different type signature: in Julia nota- tion, *(a::Number, b::Number) defines a method for scalar Table 1: Representative applications of the scan al- multiplication, whereas *(a::Matrix, b::Vector) defines gorithm, employing four basic types of operations: a method for matrix-vector products, and so on. Closely re- addition, maximization, multiplication, and function lated is the support for multiple dispatch, where the method composition. chosen to match an expression like a*b can depend on the type of more than one argument. In this respect, multi- methods differs greatly from more conventional languages reused for other operators like multiplication (*), maximiza- that provide class-based objects; dispatch only occurs on tion (max) [43], or even string concatenation2. The ! suffix the first argument, which is the type of the class. is a Julia convention denoting that the function mutates at In this paper, we demonstrate how multimethod polymor- least one of its arguments; in this case, the cumulative sums phism is far more general than just dispatching on compu- are computed in-place on y. tational kernels. We study one specific algorithm, namely The general problem is called the prefix sum [6, 8] or scan, and show how the same exact code written in Julia scan [26, 27]. Nominally, it appears that the data has to for serial computation can be composed with appropriately be scanned in one sweep from first to last and is a naturally overloaded operators to generate parallel code, visualiza- serial process. However, the insight behind parallel prefix al- tions, and also proofs of correctness. gorithms [6, 9, 30, 31, 32, 44] is that associativity allows the operations to regrouped in different ways which can expose 1.2 The scan algorithm potential for concurrent execution, which can be interpreted The basic problem of interest is to compute from some as generic divide-and-conquer strategies for recursive com- initial data y the partial sums z such that: putation [45]. In its general form, scan algorithms can be computed as a z[1] = y[1] higher-order function which takes as input some associative z[2] = y[1] + y[2] operator. Table 1 shows a representative list of applications z[3] = y[1] + y[2] + y[3] of parallel prefix, showing the diversity of applications and ... associative operators associated with those applications [7, 8]. Scan is therefore a prime example of an algorithm that One way to compute this sum efficiently is to note the prefix can exploit polymorphism for genericity. property, i.e. that the kth partial sum depends only on the 1.3 The Brent–Kung form of parallel prefix (k − 1)th partial sum and the kth element: In this paper, we focus on the Brent{Kung form [9] of z[1] = y[1] parallel prefix, where the computation is organized into two z[2] = z[1] + y[2] trees. For simplicity, we present first the special case of z[3] = z[2] + y[3] parallel prefix for n = 8 data points. ... function prefix8!(y, +) which leads to the simple algorithm: length(y)==8 || error("length 8 only") for i in [2,4,6,8] y[i] = y[i-1] + y[i] end for i in [ 4, 8] y[i] = y[i-2] + y[i] end function prefix_serial!(y, +) for i=2:length(y) 2As written, the prefix_serial! function assumes, but y[i] = y[i-1] + y[i] does not check, that the function passed to it is associative. end If necessary, checks of the form y @assert (y[1]+y[2])+y[3] == y[1]+(y[2]+y[3]) end can be included, but for simplicity of presentation, we omit such checks from the code presented in this paper. We also The cumulative sum problem generalizes to any associa- neglect concerns relating to approximate associativity, such tive operator; in this Julia function, the argument + spec- as roundoff errors in floating-point addition or multiplica- ifies the operator of interest, allowing the same code to be tion [34]. for i in [ 8] y[i] = y[i-4] + y[i] end render(prefix_serial!(AccessArray(8),+)) for i in [ 6 ] y[i] = y[i-2] + y[i] end for i in [ 3,5,7 ] y[i] = y[i-1] + y[i] end y end Figure 1 illustrates the difference between the number and order of operations in prefix_serial! and prefix8!.
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