Programmers, scientific and otherwise, spend a lot of time discussing which programming languages, libraries, and development tools to use. In such discussions, the notion of a programming paradigm is rarely mentioned, and yet it is a very fundamental one. It becomes particularly important for parallel and concurrent programming, where the most popular languages and libraries do not necessarily provide the best programming paradigm. In this post, I will explain what a programming paradigm is and why its choice matters more than the choice of a language.
A programming paradigm defines a general approach to writing programs. It defines the concepts and abstractions in terms of which a program is expressed. For example, a programming paradigm defines how data is described, how control flow is handled, how program elements are composed, etc. Well-known programming paradigms are structured programming, object-oriented programming, and functional programming.
The implementation of a programming paradigm consists of a programming language, its runtime system, libraries, and sometimes coding conventions. Some programming languages are optimized for a specific paradigm, whereas others are explicitly designed to support multiple paradigms. Paradigms that the language designer did not have in mind can sometimes be implemented by additional conventions, libraries, or preprocessors.
The list of programming paradigms that have been proposed and/or used is already quite long (see the Wikipedia entry, for example), but the ones that are practically important and significantly distinct are much less numerous. A good overview and comparison is given in the book chapter “Programming paradigms for dummies” by Peter van Roy. I will concentrate on one aspect discussed in van Roy’s text (look at section 6 in particular), which I consider of particular relevance for scientific computing: determinism.
A deterministic programming paradigm is one in which every possible program has a fully deterministic behaviour: given the same input, it executes its steps in the same order and produces the same output. This is in fact what most of us would intuitively expect from a computer program. However, there are useful programs that could not be written with this restriction. A Web server, for example, has to react to external requests which are outside of its control, and take into account resource usage (e.g. database access) and possible network errors in deciding when and in which order to process requests. This shows that there is a need for non-deterministic programming paradigms. For the vast majority of scientific applications, however, determinism is a requirement, and a programming paradigm that enforces determinism is a big help in avoiding bugs. Most scientific applications that run serially have been written using a deterministic programming paradigm, as implemented by most of the popular programming languages.
Parallel computing has changed the situation significantly. When several independent processors work together on the execution of an algorithm, fully deterministic behavior is no longer desirable, as it would imply frequent synchronizations of all processors. The precise order in which independent operations are executed is typically left unspecified by a program. What matters is that the output of the program is determined only by the input. As long as this is guaranteed, it is acceptable and even desirable to let compilers and run-time systems optimize the scheduling of individual subtasks. In Peter van Roy’s classification, this distinction is called “observable” vs. “non-observable” non-determinism. A programming paradigm for scientific computing should permit non-determinism, but should exclude observable non-determinism. While observable non-determinism makes the implementation of certain programs (such as Web servers) possible, it also opens the way to bugs that are particularly nasty to track down: deadlocks, race conditions, results that change with the number of processors or when moving from one parallel machine to another one.
Unfortunately, two of the most popular programming paradigms for parallel scientific applications do allow observable non-determinism: message passing, as implemented by the MPI library, and multi-threading. Those who have used either one have probably suffered the consequences. The problem is thus well known, but the solutions aren’t. Fortunately, they do exist: there are several programming paradigms that encapsulate non-determinism in such a way that it cannot influence the results of a program. One of them is widely known and used: OpenMP, which is a layer above multi-threading that guarantees deterministic results. However, OpenMP is limited to shared-memory multiprocessor machines.
For the at least as important category of distributed-memory parallel machines, there are also programming paradigms that don’t have the non-deterministic features of message passing, and they are typically implemented as a layer above MPI. One example is the BSP model, which I have presented in an article in the magazine Computing in Science and Engineering. Another example is the parallel skeletons model, presented by Joël Falcou in the same magazine. Unfortunately, these paradigms are little known and not well supported by programming tools. As a consequence, most scientific applications for distributed-memory machines are written using the message passing paradigm.
Finally, a pair of programming paradigms discussed by van Roy deserves special mention, because it might well become important in scientific computing in the near future: functional programming and declarative concurrency. I have written about functional programming earlier; its main advantage is the possibility to apply mathematical reasoning and automatic transformations to program code, leading to better code (in the sense of correctness) and to better optimization techniques. Declarative concurrency is functional programming plus annotations for parallelization. The nice feature is that these annotations (not very different in principle from OpenMP pragmas) don’t change the result of the program, they only influence its performance on a parallel machine. Starting from a correct functional program, it is thus possible to obtain an equivalent parallel one by automatic or manual (but automatically verified) transformations that is guaranteed to behave identically except for performance. Correctness and performance can thus be separated, which should be a big help in writing correct and efficient parallel programs. I say “should” because this approach hasn’t been used very much, and isn’t supported yet by any mainstream programming tools. This may change in a couple of years, so you might want to watch out for developments in this area.