Graph Analytics Framework Research Articles

Examining Disease Multimorbidity in U.S. Hospital Visits Before and During COVID-19 Pandemic: A Graph Analytics Approach

Enduring effects of the COVID-19 pandemic on healthcare systems can be preempted by identifying patterns in diseases recorded in hospital visits over time. Disease multimorbidity or simultaneous occurrence of multiple diseases is a growing global public health challenge as populations age and long-term conditions become more prevalent. We propose a graph analytics framework for analyzing disease multimorbidity in hospital visits. Within the framework, we propose a graph model to explain multimorbidity as a function of prevalence, category, and chronic nature of the underlying disease. We apply our model to examine and compare multimorbidity patterns in public hospitals in Arizona, U.S., during five six-month time periods before and during the pandemic. We observe that while multimorbidity increased by 34.26% and 41.04% during peak pandemic for mental disorders and respiratory disorders respectively, the gradients for endocrine diseases and circulatory disorders were not significant. Multimorbidity for acute conditions is observed to be decreasing during the pandemic while multimorbidity for chronic conditions remains unchanged. Our graph analytics framework provides guidelines for empirical analysis of disease multimorbidity using electronic health records. The patterns identified using our proposed graph model informs future research and healthcare policy makers for pre-emptive decision making.

Gretch

Data-dependent memory accesses (DDAs) pose an important challenge for high-performance graph analytics (GA). This is because such memory accesses do not exhibit enough temporal and spatial locality resulting in low cache performance. Prior efforts that focused on improving the performance of DDAs for GA are not applicable across various GA frameworks. This is because (1) they only focus on one particular graph representation, and (2) they require workload changes to communicate specific information to the hardware for their effective operation. In this work, we propose a hardware-only solution to improving the performance of DDAs for GA across multiple GA frameworks. We present a hardware prefetcher for GA called Gretch, that addresses the above limitations. An important observation we make is that identifying certain DDAs without hardware-software communication is sensitive to the instruction scheduling. A key contribution of this work is a hardware mechanism that activates Gretch to identify DDAs when using either in-order or out-of-order instruction scheduling. Our evaluation shows that Gretch provides an average speedup of 38% over no prefetching, 25% over conventional stride prefetcher, and outperforms prior DDAs prefetchers by 22% with only 1% increase in power consumption when executed on different GA workloads and frameworks.

Fast load balance parallel graph analytics with an automatic graph data structure selection algorithm

This paper investigates the performance of graph-structured analytics on large-scale shared memory systems. Graph analytics are highly demanding for efficient graph traversal due to large data set size and irregular data access patterns. In order to achieve efficient graph analytics, we consider and discuss the performance of three common types of graph data structures. Also, we demonstrate that load balance is to a large extent determined by the number of edges and number of unique vertices processed by each thread. Finally, we propose an automatic graph data structure selection algorithm and an efficient reordering as a pre-processing step to balance the number of vertices and edges together. Reordering algorithm also optimally balances edges and vertices for graphs with a power-law degree distribution and ensures an equal degree distribution across threads. The developed techniques are implemented in GraphGrind, a new shared memory graph analytics framework. Evaluation in GraphGrind, shows that this outperforms state-of-the-art graph analytics frameworks for shared memory including Ligra (Shun and Blelloch, 2013) up to 10.4×, and Polymer (Zhang et al., 2015) up to 8.3× across 8 algorithms and 6 graphs.

Single machine graph analytics on massive datasets using Intel optane DC persistent memory

Intel Optane DC Persistent Memory (Optane PMM) is a new kind of byte-addressable memory with higher density and lower cost than DRAM. This enables the design of affordable systems that support up to 6TB of randomly accessible memory. In this paper, we present key runtime and algorithmic principles to consider when performing graph analytics on extreme-scale graphs on Optane PMM and highlight principles that can apply to graph analytics on all large-memory platforms. To demonstrate the importance of these principles, we evaluate four existing shared-memory graph frameworks and one out-of-core graph framework on large real-world graphs using a machine with 6TB of Optane PMM. Our results show that frameworks using the runtime and algorithmic principles advocated in this paper (i) perform significantly better than the others and (ii) are competitive with graph analytics frameworks running on production clusters.

Capability for Multi-Core and Many-Core Memory Systems: A Case-Study With Xeon Processors

The general volume of data has exploded to unimaginable levels in the past decade. Therefore, the big data analytics has become an area of focus. Many frameworks have been developed for data analytics, such as Hadoop, Spark, etc. Most of the frameworks are built on multi-core or many-core memory systems, requiring developers and users to have an in-depth understanding of the architectures to take full advantage of the hardware. In this paper, we present a comprehensive study of both multi-core and many-core memory systems and discuss the different characteristics, including core, cache, memory, and the on-chip network. Furthermore, we propose a simple but effective mechanism for cache false-sharing overhead that can reduce a large number of LLC-load/store instructions and LLC cache-misses. In addition, we conduct detailed experiments with Ligra, a graph analytics framework, on four different-sized datasets. The results show that it can achieve up to a 2.5× and 9.5× speed-up for multi-core and many-core memory systems, respectively. We finally share our key findings and discuss the platform development on multi-core and many-core memory systems.

HPGraph: High-Performance Graph Analytics with Productivity on the GPU

The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs. Existing GPU implementations have limited performance with compromising on productivity. HPGraph, our high-performance bulk-synchronous graph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to generalized sparse matrix operations on GPU as the backend. HPGraph strikes a balance between performance and productivity by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model for users to implement various graph algorithms with relatively little effort. We evaluate the performance of HPGraph for four graph primitives (BFS, SSSP, PageRank, and TC). Our experiments show that HPGraph matches or even exceeds the performance of high-performance GPU graph libraries such as MapGraph, nvGraph, and Gunrock. HPGraph also runs significantly faster than advanced CPU graph libraries.

Cost‐efficient and network‐aware dynamic repartitioning‐based algorithms for scheduling large‐scale graphs in cloud computing environments

SummaryLarge amount of data that is generated by Internet and enterprize applications are stored in the form of graphs. Graph processing systems are broadly used in enterprizes to process such data. With the rapid growth in mobile and social applications and complicated connections of Internet websites, massive concurrent operations need to be handled. On the other hand, the intrinsic structure and the size of real‐world graphs make distributed processing of graphs more challenging. Low balanced communication and computation, low preprocessing overhead, low memory footprint, and scalability should be offered by distributed graph analytics frameworks. Moreover, the effects of network factors such as bandwidth and traffic as well as monetary cost of processing such large‐scale graphs and the mutual impact of these elements have been less studied. To address these issues, we proposed two dynamic repartitioning algorithms that consider network factors, affecting public cloud environments to decrease the monetary cost of processing. A new classification of graph algorithms and processing is also introduced, which will be used to choose the best strategy for processing at any operation. We plugged these algorithms to our extended graph processing system (iGiraph) and compared them with those supported in other graph processing systems such as Giraph and Surfer on Australian National Cloud Infrastructure. We observed that up to 30% faster execution time, up to 50% network traffic decline, and more than 50% cost reduction are achieved by our algorithms compared to a framework such as the popular Giraph.

Analyzing Enterprise Storage Workloads With Graph Modeling and Clustering

Utilizing graph analysis models and algorithms to exploit complex interactions over a network of entities is emerging as an attractive network analytic technology. In this paper, we show that traditional column or row-based trace analysis may not be effective in deriving deep insights hidden in the storage traces collected over complex storage applications, such as complex spatial and temporal patterns, hotspots and their movement patterns. We propose a novel graph analytics framework, GraphLens, for mining and analyzing real world storage traces with three unique features. First, we model storage traces as heterogeneous trace graphs in order to capture multiple complex and heterogeneous factors, such as diverse spatial/temporal access information and their relationships, into a unified analytic framework. Second, we employ and develop an innovative graph clustering method that employs two levels of clustering abstractions on storage trace analysis. We discover interesting spatial access patterns and identify important temporal correlations among spatial access patterns. This enables us to better characterize important hotspots and understand hotspot movement patterns. Third, at each level of abstraction, we design a unified weighted similarity measure through an iterative dynamic weight learning algorithm. With an optimal weight assignment scheme, we can efficiently combine the correlation information for each type of storage access patterns, such as random versus sequential, read versus write, to identify interesting spatial/temporal correlations hidden in the traces. Some optimization techniques on matrix computation are proposed to further improve the efficiency of our clustering algorithm on large trace datasets. Extensive evaluation on real storage traces shows GraphLens can provide broad and deep trace analysis for better storage strategy planning and efficient data placement guidance. GraphLens can be applied to both a single PC with multiple disks and a distributed network across a cluster of compute nodes to offer a few opportunities for optimization of storage performance.

GraphMat

Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We thus get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is a single-node multicore graph framework written in C++ which has enabled us to write a diverse set of graph algorithms with the same effort compared to other vertex programming frameworks. GraphMat performs 1.1-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. GraphMat also matches the performance of MapGraph, a GPU-based graph framework, despite running on a CPU platform with significantly lower compute and bandwidth resources. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMat can naturally benefit from the trend of increasing parallelism in future hardware.