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2.4. Common Approaches to Concurrent and Parallel Programming
proposed by Dean and Ghemawat [2004] includes mechanisms to provide
fault tolerance and scalability to utilize large clusters. It also extends the basic
map/reduce idea with notions of combiner functions and they describe how
to support side-effects for map and reduce operators. The side-effects are
however restricted to being idempotent and atomic to ensure deterministic
results in the case of failure and recomputation. Side-effects on the input data
themselves are however not supported. Instead, the input data are considered
to be immutable for the overall process.
Compared to fork/join, MapReduce exposes the developer much less to
the aspect of control flow. Instead, it requires only input data and a set of operators
that are applied in a predefined order, without making any promises
about the order in which the input data are processed.
Data-flow Programming languages attempt to move entirely to data-dependency
based program representation. Languages such as Lucid [Ashcroft
and Wadge, 1977] do not regard the sequential notation as imperative to
the order of program execution. Instead, programs are evaluated in a lazy,
demand-driven manner.
Other languages such as StreamIt [Thies et al., 2002] make data dependencies
even more explicit by reifying the notion of data streams to which a set
of kernel functions is applied. These programming languages enable the explicit
encoding of data dependencies, which can then be used by optimizing
compilers to generate highly efficient code that exploits the available data
parallelism in the application.
2.4.6. Summary
This section introduced a partial taxonomy to categorize concurrent and parallel
programming concepts, because Flynn’s and Almasi and Gottlieb [1994]’s
taxonomies do not reflect the common approaches to concurrent and parallel
programming in a way that facilitates their discussion in the context of
this dissertation. Therefore, this dissertation proposes to categorize concurrent
and parallel programming concepts into communicating threads, communicating
isolates, and data parallelism. For each of these categories, this section
discussed a number of common approaches.
Clojure agents are highlighted and discussed in more detail, because Chapter
5 and Chapter 6 rely on them as a running example.
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