arXiv subjects

Lukas Gianinazzi

Publications and source records attributed to Lukas Gianinazzi.

26 records · Page 2Linked to original sources

arXiv2021

Parallel Algorithms for Finding Large Cliques in Sparse Graphs

We present a parallel k-clique listing algorithm with improved work bounds (for the same depth) in sparse graphs with low degeneracy or arboricity. We achieve this by introducing and analyzing a new pruning criterion for a backtracking search. Our algorithm has better asymptotic performance, especially for larger cliques (when k is not constant), where we avoid the straightforwardly exponential runtime growth with respect to the clique size. In particular, for cliques that are a constant factor smaller than the graph's degeneracy, the work improvement is an exponential factor in the clique size compared to previous results. Moreover, we present a low-depth approximation to the community degeneracy (which can be arbitrarily smaller than the degeneracy). This approximation enables a low depth clique listing algorithm whose runtime is parameterized by the community degeneracy.

cs.DS↗

arXiv2021

Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics

We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.

cs.DS↗

arXiv2021

Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs

Determining I/O lower bounds is a crucial step in obtaining communication-efficient parallel algorithms, both across the memory hierarchy and between processors. Current approaches either study specific algorithms individually, disallow programmatic motifs such as recomputation, or produce asymptotic bounds that exclude important constants. We propose a novel approach for obtaining precise I/O lower bounds on a general class of programs, which we call Simple Overlap Access Programs (SOAP). SOAP analysis covers a wide variety of algorithms, from ubiquitous computational kernels to full scientific computing applications. Using the red-blue pebble game and combinatorial methods, we are able to bound the I/O of the SOAP-induced Computational Directed Acyclic Graph (CDAG), taking into account multiple statements, input/output reuse, and optimal tiling. To deal with programs that are outside of our representation (e.g., non-injective access functions), we describe methods to approximate them with SOAP. To demonstrate our method, we analyze 38 different applications, including kernels from the Polybench benchmark suite, deep learning operators, and -- for the first time -- applications in unstructured physics simulations, numerical weather prediction stencil compositions, and full deep neural networks. We derive tight I/O bounds for several linear algebra kernels, such as Cholesky decomposition, improving the existing reported bounds by a factor of two. For stencil applications, we improve the existing bounds by a factor of up to 14. We implement our method as an open-source tool, which can derive lower bounds directly from provided C code.

cs.CC↗

arXiv2021

GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds.

cs.DC↗

arXiv2020

Parametric Graph Templates: Properties and Algorithms

Hierarchical structure and repetition are prevalent in graphs originating from nature or engineering. These patterns can be represented by a class of parametric-structure graphs, which are defined by templates that generate structure by way of repeated instantiation. We propose a class of parametric graph templates that can succinctly represent a wide variety of graphs. Using parametric graph templates, we develop structurally-parametric algorithm variants of maximum flow, minimum cut, and tree subgraph isomorphism. Our algorithms are polynomial time for maximum flow and minimum cut and are fixed-parameter tractable for tree subgraph isomorphism when parameterized by the size of the tree subgraph. By reasoning about the structure of the repeating subgraphs, we avoid explicit construction of the instantiation. Furthermore, we show how parametric graph templates can be recovered from an instantiated graph in quasi-polynomial time when certain parameters of the graph are bounded. Parametric graph templates and the presented algorithmic techniques thus create opportunities for reasoning about the generating structure of a graph, rather than an instance of it.

cs.DS↗

arXiv2020

High-Performance Parallel Graph Coloring with Strong Guarantees on Work, Depth, and Quality

We develop the first parallel graph coloring heuristics with strong theoretical guarantees on work and depth and coloring quality. The key idea is to design a relaxation of the vertex degeneracy order, a well-known graph theory concept, and to color vertices in the order dictated by this relaxation. This introduces a tunable amount of parallelism into the degeneracy ordering that is otherwise hard to parallelize. This simple idea enables significant benefits in several key aspects of graph coloring. For example, one of our algorithms ensures polylogarithmic depth and a bound on the number of used colors that is superior to all other parallelizable schemes, while maintaining work-efficiency. In addition to provable guarantees, the developed algorithms have competitive run-times for several real-world graphs, while almost always providing superior coloring quality. Our degeneracy ordering relaxation is of separate interest for algorithms outside the context of coloring.

cs.DS↗

arXiv2020

Parallel Planar Subgraph Isomorphism and Vertex Connectivity

We present the first parallel fixed-parameter algorithm for subgraph isomorphism in planar graphs, bounded-genus graphs, and, more generally, all minor-closed graphs of locally bounded treewidth. Our randomized low depth algorithm has a near-linear work dependency on the size of the target graph. Existing low depth algorithms do not guarantee that the work remains asymptotically the same for any constant-sized pattern. By using a connection to certain separating cycles, our subgraph isomorphism algorithm can decide the vertex connectivity of a planar graph (with high probability) in asymptotically near-linear work and poly-logarithmic depth. Previously, no sub-quadratic work and poly-logarithmic depth bound was known in planar graphs (in particular for distinguishing between four-connected and five-connected planar graphs).

cs.DS↗

arXiv2020

Parallel Minimum Cuts in Near-linear Work and Low Depth

We present the first near-linear work and poly-logarithmic depth algorithm for computing a minimum cut in a graph, while previous parallel algorithms with poly-logarithmic depth required at least quadratic work in the number of vertices. In a graph with $n$ vertices and $m$ edges, our algorithm computes the correct result with high probability in $O(m {\log}^4 n)$ work and $O({\log}^3 n)$ depth. This result is obtained by parallelizing a data structure that aggregates weights along paths in a tree and by exploiting the connection between minimum cuts and approximate maximum packings of spanning trees. In addition, our algorithm improves upon bounds on the number of cache misses incurred to compute a minimum cut.

cs.DC↗