Automated detection of structured coarse-grained parallelism in sequential legacy applications
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Date
27/11/2014Author
Edler Von Koch, Tobias Joseph Kastulus
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Abstract
The efficient execution of sequential legacy applications on modern, parallel computer
architectures is one of today’s most pressing problems. Automatic parallelization
has been investigated as a potential solution for several decades but its success
generally remains restricted to small niches of regular, array-based applications.
This thesis investigates two techniques that have the potential to overcome these
limitations.
Beginning at the lowest level of abstraction, the binary executable, it presents
a study of the limits of Dynamic Binary Parallelization (Dbp), a recently proposed
technique that takes advantage of an underlying multicore host to transparently
parallelize a sequential binary executable. While still in its infancy, Dbp has received
broad interest within the research community. This thesis seeks to gain an
understanding of the factors contributing to the limits of Dbp and the costs and
overheads of its implementation. An extensive evaluation using a parameterizable
Dbp system targeting a Cmp with light-weight architectural Tls support is presented.
The results show that there is room for a significant reduction of up to 54%
in the number of instructions on the critical paths of legacy Spec Cpu2006 benchmarks,
but that it is much harder to translate these savings into actual performance
improvements, with a realistic hardware-supported implementation achieving a
speedup of 1.09 on average.
While automatically parallelizing compilers have traditionally focused on data
parallelism, additional parallelism exists in a plethora of other shapes such as task
farms, divide & conquer, map/reduce and many more. These algorithmic skeletons,
i.e. high-level abstractions for commonly used patterns of parallel computation,
differ substantially from data parallel loops. Unfortunately, algorithmic skeletons
are largely informal programming abstractions and are lacking a formal characterization
in terms of established compiler concepts. This thesis develops compiler-friendly
characterizations of popular algorithmic skeletons using a novel notion of
commutativity based on liveness. A hybrid static/dynamic analysis framework for
the context-sensitive detection of skeletons in legacy code that overcomes limitations
of static analysis by complementing it with profiling information is described.
A proof-of-concept implementation of this framework in the Llvm compiler infrastructure
is evaluated against Spec Cpu2006 benchmarks for the detection of a typical skeleton. The results illustrate that skeletons are often context-sensitive in
nature.
Like the two approaches presented in this thesis, many dynamic parallelization
techniques exploit the fact that some statically detected data and control
flow dependences do not manifest themselves in every possible program execution
(may-dependences) but occur only infrequently, e.g. for some corner cases, or not
at all for any legal program input. While the effectiveness of dynamic parallelization
techniques critically depends on the absence of such dependences, not much
is known about their nature. This thesis presents an empirical analysis and characterization
of the variability of both data dependences and control flow across
program runs. The cBench benchmark suite is run with 100 randomly chosen
input data sets to generate whole-program control and data flow graphs (Cdfgs)
for each run, which are then compared to obtain a measure of the variance in the
observed control and data flow. The results show that, on average, the cumulative
profile information gathered with at least 55, and up to 100, different input data
sets is needed to achieve full coverage of the data flow observed across all runs.
For control flow, the figure stands at 46 and 100 data sets, respectively. This suggests
that profile-guided parallelization needs to be applied with utmost care, as
misclassification of sequential loops as parallel was observed even when up to 94
input data sets are used.