The Breadth First Search (BFS) algorithm is the foundation and building block of many higher graph-based operations such as spanning trees, shortest paths and betweenness centrality. The importance of this algorithm increases each day due to it is a key requirement for many data structures which are becoming popular nowadays. When the BFS algorithm is parallelized by distributing the graph between several processors the interconnection network limits the performance. Hence, improvements on this area may benefit the overall performance of the algorithm.
This work presents an alternative compression scheme for communications in distributed BFS processing. It focuses on BFS processors using General-Purpose Graphics Processing Units.
On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list repres…
Vectorization and GPUs will profoundly change graph processing. Traditional graph algorithms tuned for 32- or 64-bit based memory accesses will be inefficient on architectures with 512-bit wide (or larger) instruction units that are already present in the Intel Knights Landing (KNL) manycore CPU. Anticipating this shift, we propose SlimSell: a vectorizable graph representation to accelerate Breadth-First Search (BFS) based on sparse-matrix dense-vector (SpMV) products. SlimSell extends and combines the state-of-the-art SIMD-friendly Sell-C-sigma matrix storage format with tropical, real, boole…
In this paper, we propose a GPU-efficient subgraph isomorphism algorithm using the Gunrock graph analytic framework, GSM (Gunrock Subgraph Matching), to compute graph matching on GPUs. In contrast to previous approaches on the CPU which are based on depth-first traversal, GSM is BFS-based: possible matches are explored simultaneously in a breadth-first strategy. The advantage of using BFS-based traversal is that we can leverage the massively parallel processing capabilities of the GPU. The disadvantage is the generation of more intermediate results. We propose several optimization techniques t…
K Nearest Neighbor (KNN) joins are used in scientific domains for data analysis, and are building blocks of several well-known algorithms. KNN-joins find the KNN of all points in a dataset. This paper focuses on a hybrid CPU/GPU approach for low-dimensional KNN-joins, where the GPU may not yield substantial performance gains over parallel CPU algorithms. We utilize a work queue that prioritizes computing data points in high density regions on the GPU, and low density regions on the CPU, thereby taking advantage of each architecture's relative strengths. Our approach, HybridKNN-Join, effectivel…
Graph Pattern Mining (GPM) extracts higher-order information in a large graph by searching for small patterns of interest. GPM applications are computationally expensive, and thus attractive for GPU acceleration. Unfortunately, due to the complexity of GPM algorithms and parallel hardware, hand optimizing GPM applications suffers programming complexity, while existing GPM frameworks sacrifice efficiency for programmability. Moreover, little work has been done on GPU to scale GPM computation to large problem sizes. We describe G2Miner, the first Graph Pattern Mining (GPM) framework that runs on…
With high computation power and memory bandwidth, graphics processing units (GPUs) lend themselves to accelerate data-intensive analytics, especially when such applications fit the single instruction multiple data (SIMD) model. However, graph algorithms such as breadth-first search and k-core, often fail to take full advantage of GPUs, due to irregularity in memory access and control flow. To address this challenge, we have developed SIMD-X, for programming and processing of single instruction multiple, complex, data on GPUs. Specifically, the new Active-Compute-Combine (ACC) model not only pr…
Breadth-First Search (BFS) is a building block used in a wide array of graph analytics and is used in various network analysis domains: social, road, transportation, communication, and much more. Over the last two decades, network sizes have continued to grow. The popularity of BFS has brought with it a need for significantly faster traversals. Thus, BFS algorithms have been designed to exploit shared-memory and shared-nothing systems -- this includes algorithms for accelerators such as the GPU. GPUs offer extremely fast traversals at the cost of processing smaller graphs due to their limited …
Graph Traversal on Tensor Cores: A BFS Framework for Modern GPUs
Department of Computer Science and Engineering, Faculty of Engineering and Natural Sciences, Sabancı University（萨班哲大学工程与自然科学学院计算机科学与工程系）；Center of Excellence in Data Analytics, Sabancı University（萨班哲大学数据卓越中心）
The self-join finds all objects in a dataset within a threshold of each other defined by a similarity metric. As such, the self-join is a building block for the field of databases and data mining, and is employed in Big Data applications. In this paper, we advance a GPU-efficient algorithm for the similarity self-join that uses the Euclidean distance metric. The search-and-refine strategy is an efficient approach for low dimensionality datasets, as index searches degrade with increasing dimension (i.e., the curse of dimensionality). Thus, we target the low dimensionality problem, and compare o…
We factor Beamer's push-pull, also known as direction-optimized breadth-first-search (DOBFS) into 3 separable optimizations, and analyze them for generalizability, asymptotic speedup, and contribution to overall speedup. We demonstrate that masking is critical for high performance and can be generalized to all graph algorithms where the sparsity pattern of the output is known a priori. We show that these graph algorithm optimizations, which together constitute DOBFS, can be neatly and separably described using linear algebra and can be expressed in the GraphBLAS linear-algebra-based framework.…
High-performance analysis of unstructured data like graphs now is critical for applications ranging from business intelligence to genome analysis. Towards this, data centers hold large graphs in memory to serve multiple concurrent queries from different users. Even a single analysis often explores multiple options. Current computing architectures often are not the most time- or energy-efficient solutions. The novel Lucata Pathfinder architecture tackles this problem, combining migratory threads for low-latency reading with memory-side processing for high-performance accumulation. One hundred t…
RouterWise: Joint Resource Allocation and Routing for Latency-Aware Multi-Model LLM Serving
DisNet Lab, School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息学院）；School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息学院）；Department of Data Science & AI, Monash University（莫纳什大学数据科学与人工智能系）
