Geographic information systems deal with spatial data and its analysis. Spatial data contains many attributes with location information. Spatial autocorrelation is a fundamental concept in spatial analysis. It suggests that similar objects tend to cluster in geographic space. Hotspots, an example of autocorrelation, are statistically significant clusters of spatial data. Other autocorrelation measures like Moran’s I are used to quantify spatial dependence.
Large scale spatial autocorrelation methods are compute- intensive. Fast methods for hotspots detection and analysis are crucial in recent times of COVID-19 pandemic. Therefore, we have developed parallelization methods on heterogeneous CPU and GPU environments. To the best of our knowledge, this is the first GPU and SIMD-based design and implementation of autocorrelation kernels. Earlier methods in literature introduced cluster-based and MapReduce-based parallelization. We have used Intrinsics to exploit SIMD parallelism on x86 CPU architecture. We have used MPI Graph Topology to minimize inter-process communication.
Our benchmarks for CPU/GPU optimizations gain up to 750X relative speedup with a 8 GPU setup when compared to baseline sequential implementation. Compared to the best implementation using OpenMP + R-tree data structure on a single compute node, our accelerated hotspots benchmark gains a 25X speedup. For real world US counties and COVID data evolution calculated over 500 days, we gain up to 110X speedup reducing time from 33 minutes to 0.3 minutes.
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ADAPT: An Event-Based Adaptive Collective Communication Framework
The increase in scale and heterogeneity of high-performance computing (HPC) systems predispose the performance of Message Passing Interface (MPI) collective communications to be susceptible to noise, and to adapt to a complex mix of hardware capabilities. The designs of state of the art MPI collectives heavily rely on synchronizations; these designs magnify noise across the participating processes, resulting in significant performance slowdown. Therefore, such design philosophy must be reconsidered to efficiently and robustly run on the large-scale heterogeneous platforms. In this paper, we present ADAPT, a new collective communication framework in Open MPI, using event-driven techniques to morph collective algorithms to heterogeneous environments. The core concept of ADAPT is to relax synchronizations, while mamtaining the minimal data dependencies of MPI collectives. To fully exploit the different bandwidths of data movement lanes in heterogeneous systems, we extend the ADAPT collective framework with a topology-aware communication tree. This removes the boundaries of different hardware topologies while maximizing the speed of data movements. We evaluate our framework with two popular collective operations: broadcast and reduce on both CPU and GPU clusters. Our results demonstrate drastic performance improvements and a strong resistance against noise compared to other state of the art MPI libraries. In particular, we demonstrate at least 1.3X and 1.5X speedup for CPU data and 2X and 10X speedup for GPU data using ADAPT event-based broadcast and reduce operations.
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- Award ID(s):
- 1664142
- NSF-PAR ID:
- 10062120
- Date Published:
- Journal Name:
- The 27th International Symposium on High-Performance Parallel and Distributed Computing
- Page Range / eLocation ID:
- 118 to 130
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3208040.3208054
