5 research outputs found
Structured parallelism discovery with hybrid static-dynamic analysis and evaluation technique
Parallel computer architectures have dominated the computing landscape for the
past two decades; a trend that is only expected to continue and intensify, with increasing specialization and heterogeneity. This creates huge pressure across the software
stack to produce programming languages, libraries, frameworks and tools which will
efficiently exploit the capabilities of parallel computers, not only for new software, but
also revitalizing existing sequential code. Automatic parallelization, despite decades of
research, has had limited success in transforming sequential software to take advantage
of efficient parallel execution. This thesis investigates three approaches that use commutativity analysis as the enabler for parallelization. This has the potential to overcome
limitations of traditional techniques.
We introduce the concept of liveness-based commutativity for sequential loops.
We examine the use of a practical analysis utilizing liveness-based commutativity in a
symbolic execution framework. Symbolic execution represents input values as groups
of constraints, consequently deriving the output as a function of the input and enabling
the identification of further program properties. We employ this feature to develop an
analysis and discern commutativity properties between loop iterations. We study the
application of this approach on loops taken from real-world programs in the OLDEN
and NAS Parallel Benchmark (NPB) suites, and identify its limitations and related
overheads.
Informed by these findings, we develop Dynamic Commutativity Analysis (DCA), a
new technique that leverages profiling information from program execution with specific
input sets. Using profiling information, we track liveness information and detect loop
commutativity by examining the code’s live-out values. We evaluate DCA against almost
1400 loops of the NPB suite, discovering 86% of them as parallelizable. Comparing
our results against dependence-based methods, we match the detection efficacy of two
dynamic and outperform three static approaches, respectively. Additionally, DCA is
able to automatically detect parallelism in loops which iterate over Pointer-Linked
Data Structures (PLDSs), taken from wide range of benchmarks used in the literature,
where all other techniques we considered failed. Parallelizing the discovered loops, our
methodology achieves an average speedup of 3.6× across NPB (and up to 55×) and up
to 36.9× for the PLDS-based loops on a 72-core host. We also demonstrate that our
methodology, despite relying on specific input values for profiling each program, is able
to correctly identify parallelism that is valid for all potential input sets.
Lastly, we develop a methodology to utilize liveness-based commutativity, as implemented in DCA, to detect latent loop parallelism in the shape of patterns. Our approach
applies a series of transformations which subsequently enable multiple applications
of DCA over the generated multi-loop code section and match its loop commutativity
outcomes against the expected criteria for each pattern. Applying our methodology on
sets of sequential loops, we are able to identify well-known parallel patterns (i.e., maps,
reduction and scans). This extends the scope of parallelism detection to loops, such
as those performing scan operations, which cannot be determined as parallelizable by
simply evaluating liveness-based commutativity conditions on their original form