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Graph coloring—in a generic sense—is used to identify subsets of independent tasks in parallel scientific computing applications. Traditional coloring heuristics aim to reduce the number of colors used as that number also corresponds to the number of parallel steps in the application. However, if the color classes produced have a skew in their sizes, utilization of hardware resources becomes inefficient, especially for the smaller color classes. Equitable coloring is a theoretical formulation of coloring that guarantees a perfect balance among color classes, and its practical relaxation is referred to here as balanced coloring. In this paper, we consider balanced coloring models in the context of parallel computing. The goal is to achieve a balanced coloring of an input graph without increasing the number of colors that an algorithm oblivious to balance would have used. We propose and study multiple heuristics that aim to achieve such a balanced coloring for two variants of coloring problem, distance-1 coloring (the standard coloring problem) and partial distance-2 coloring (defined on a bipartite graph). We present parallelization approaches for multi-core and manycore architectures and cross-evaluate their effectiveness with respect to the quality of balance achieved and performance. Furthermore, we study the impact of the proposed balanced coloring heuristics on a concrete application-viz. parallel community detection, which is an example of an irregular application. In addition, we propose several extensions to our basic balancing schemes and evaluate their balancing efficacy and performance characteristics. The thorough treatment of balanced coloring presented in this paper from algorithms to application is expected to serve as a valuable resource to parallel application developers who seek to improve parallel performance of their applications using coloring. [ABSTRACT FROM AUTHOR]

We present a suite of fast and effective algorithms, encapsulated in a software package called ColPack, for a variety of graph coloring and related problems. Many of the coloring problems model partitioning needs arising in compression-based computation of Jacobian and Hessian matrices using Algorithmic Differentiation. Several of the coloring problems also find important applications in many areas outside derivative computation, including frequency assignment in wireless networks, scheduling, facility location, and concurrency discovery and data movement operations in parallel and distributed computing. The presentation in this article includes a high-level description of the various coloring algorithms within a common design framework, a detailed treatment of the theory and efficient implementation of known as well as new vertex ordering techniques upon which the coloring algorithms rely, a discussion of the package's software design, and an illustration of its usage. The article also includes an extensive experimental study of the major algorithms in the package using real-world as well as synthetically generated graphs. [ABSTRACT FROM AUTHOR]

Abstract: Many scientific applications benefit from the accurate and efficient computation of derivatives. Automatically generating these derivative computations from an applications source code offers a competitive alternative to other approaches, such as less accurate numerical approximations or labor-intensive analytical implementations. ADIC2 is a source transformation tool for generating code for computing the derivatives (e.g., Jacobian or Hessian) of a function given the C or C++ implementation of that function. Often the Jacobian or Hessian is sparse and presents the opportunity to greatly reduce storage and computational requirements in the automatically generated derivative computation. ColPack is a tool that compresses structurally independent columns of the Jacobian and Hessian matrices through graph coloring approaches. In this paper, we describe the integration of ColPack coloring capabilities into ADIC2, enabling accurate and efficient sparse Jacobian computations. We present performance results for a case study of a simulated moving bed chromatography application. Overall, the computation of the Jacobian by integrating ADIC2 and ColPack is approximately 40% faster than a comparable implementation that integrates ADOL-C and ColPack when the Jacobian is computed multiple times. [Copyright &y& Elsevier]

The computation of a sparse Hessian matrix H using automatic differentiation (AD) can be made efficient using the following four-step procedure: (1) Determine the sparsity structure of H, (2) obtain a seed matrix S that defines a column partition of H using a specialized coloring on the adjacency graph of H, (3) compute the compressed Hessian matrix B =HS, and (4) recover the numerical values of the entries of H from B. The coloring variant used in the second step depends on whether the recovery in the fourth step is direct or indirect: a direct method uses star coloring and an indirect method uses acyclic coloring. In an earlier work, we had designed and implemented effective heuristic algorithms for these two NP-hard coloring problems. Recently, we integrated part of the developed software with the AD tool ADOL-C, which has recently acquired a sparsity detection capability. In this paper, we provide a detailed description and analysis of the recovery algorithms and experimentally demonstrate the efficacy of the coloring techniques in the overall process of computing the Hessian of a given function using ADOL-C as an example of an AD tool. We also present new analytical results on star and acyclic coloring of chordal graphs. The experimental results show that sparsity exploitation via coloring yields enormous savings in runtime and makes the computation of Hessians of very large size feasible. The results also show that evaluating a Hessian via an indirect method is often faster than a direct evaluation. This speedup is achieved without compromising numerical accuracy. [ABSTRACT FROM AUTHOR]

Abstract: We present a scalable framework for parallelizing greedy graph coloring algorithms on distributed-memory computers. The framework unifies several existing algorithms and blends a variety of techniques for creating or facilitating concurrency. The latter techniques include exploiting features of the initial data distribution, the use of speculative coloring and randomization, and a BSP-style organization of computation and communication. We experimentally study the performance of several specialized algorithms designed using the framework and implemented using MPI. The experiments are conducted on two different platforms and the test cases include large-size synthetic graphs as well as real graphs drawn from various application areas. Computational results show that implementations that yield good speedup while at the same time using about the same number of colors as a sequential greedy algorithm can be achieved by setting parameters of the framework in accordance with the size and structure of the graph being colored. Our implementation is freely available as part of the Zoltan parallel data management and load-balancing library. [Copyright &y& Elsevier]

Acyclic and star coloring problems are specialized vertex coloring problems that arise in the efficient computation of Hessians using automatic differentiation or finite differencing, when both sparsity and symmetry are exploited. We present an algorithmic paradigm for finding heuristic solutions for these two NP-hard problems. The underlying common technique is the exploitation of the structure of two-colored induced subgraphs. For a graph G on n vertices and m edges, the time complexity of our star coloring algorithm is O(n...2), where ...k, a generalization of vertex degree, denotes the average number of distinct paths of length at most k edges starting at a vertex in G. The time complexity of our acyclic coloring algorithm is larger by a multiplicative factor involving the inverse of Ackermann's function. The space complexity of both algorithms is O(m). To the best of our knowledge, our work is the first practical algorithm for the acyclic coloring problem. For the star coloring problem, our algorithm uses fewer colors and is considerably faster than a previously known O(n...3)-time algorithm. Computational results from experiments on various large-size test graphs demonstrate that the algorithms are fast and produce highly effective solutions. The use of these algorithms in Hessian computation is expected to reduce overall runtime drastically. [ABSTRACT FROM AUTHOR]

Abstract: We explore the interplay between architectures and algorithm design in the context of shared-memory platforms and a specific graph problem of central importance in scientific and high-performance computing, distance-1 graph coloring. We introduce two different kinds of multithreaded heuristic algorithms for the stated, NP-hard, problem. The first algorithm relies on speculation and iteration, and is suitable for any shared-memory system. The second algorithm uses dataflow principles, and is targeted at the non-conventional, massively multithreaded Cray XMT system. We study the performance of the algorithms on the Cray XMT and two multi-core systems, Sun Niagara 2 and Intel Nehalem. Together, the three systems represent a spectrum of multithreading capabilities and memory structure. As testbed, we use synthetically generated large-scale graphs carefully chosen to cover a wide range of input types. The results show that the algorithms have scalable runtime performance and use nearly the same number of colors as the underlying serial algorithm, which in turn is effective in practice. The study provides insight into the design of high performance algorithms for irregular problems on many-core architectures. [Copyright &y& Elsevier]