Optimistic parallelism requires abstractions

Abstract

Irregular applications, which manipulate large, pointer-based data structures like graphs, are difficult to parallelize manually. Automatic tools and techniques such as restructuring compilers and runtime speculative execution have failed to uncover much parallelism in these applications, in spite of a lot of effort by the research community. These difficulties have even led some researchers to wonder if there is any coarse-grain parallelism worth exploiting in irregular applications.In this paper, we describe two real-world irregular applications: a Delaunay mesh refinement application and a graphics application that performs agglomerative clustering. By studying the algorithms and data structures used in these applications, we show that there is substantial coarse-grain, data parallelism in these applications, but that this parallelism is very dependent on the input data and therefore cannot be uncovered by compiler analysis. In principle, optimistic techniques such as thread-level speculation can be used to uncover this parallelism, but we argue that current implementations cannot accomplish this because they do not use the proper abstractions for the data structures in these programs.These insights have informed our design of the Galois system, an object-based optimistic parallelization system for irregular applications. There are three main aspects to Galois: (1) a small number of syntactic constructs for packaging optimistic parallelism as iteration over ordered and unordered sets, (2) assertions about methods in class libraries, and (3) a runtime scheme for detecting and recovering from potentially unsafe accesses to shared memory made by an optimistic computation.We show that Delaunay mesh generation and agglomerative clustering can be parallelized in a straight-forward way using the Galois approach, and we present experimental measurements to show that this approach is practical. These results suggest that Galois is a practical approach to exploiting data parallelism in irregular programs.

Using the Xeon Phi Platform to Run Speculatively-Parallelized Codes

“…[24,28,40], through the design of specific chips, or the addition of some functionalities. But there are also several software approaches that support the mentioned parallelism with no architectural changes [8,22,25,42]. In this work, we will describe some of the software approaches, and propose a number of possible hardware additions which might improve the performance of TLS on the Intel Xeon Phi coprocessors.…”

“…Kulkarni et al [25,26], introduced Galois, a system to support complex pointerbased sets of elements in optimistic parallelism. They were centered on parallelize applications with complex structures as linked lists, graphs, trees, etc.…”

“…Thread-level speculation (TLS) [8,34,35,42], also called speculative parallelization (SP) [14,18,22,46] or optimistic parallelism [25,26] tries to extract parallelism of loops that can not be considered fully parallel at compile time. TLS optimistically assumes that dependence violations will not occur, launching the parallel execution of the loop.…”

A quantitative study of irregular programs on GPUs

“…However, it does suffer from bank conflicts and especially thread divergence, both of which are caused by data dependent behavior, i.e., how well each word can be compressed. • Delaunay Mesh Refinement (DMR): This is a meshrefinement algorithm from computational geometry [11], [29]. It works on a triangulated input mesh in which some triangles do not conform to certain quality constraints.…”

Exploiting approximate value locality for data synchronization on multi-core processors

“…A more recent work [8] work proposes Transaction Collection Classes that use multi-level transactions and open nesting, through which concurrency can be improved by relaxing isolation when full serializability is not required. In [22], the authors propose new programming constructs to improve parallelism by exploiting the semantic commutativity of certain methods invocations.…”