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1. MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data

   https://doi.org/10.1145/3225058.3225105  

   Puri, Satish ; Paudel, Anmol ; Prasad, Sushil K. ( August 2018 , Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018).)

   In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO, a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms. Performance evaluation is done on Lustre and GPFS filesystems. MPI-Vector-IO scales well with MPI processes and file size and achieves bandwidth up to 22 GB/s for common spatial data access patterns. We observed that independent file read functions performed better than collective functions in MPI-IO for contiguous access pattern on Lustre. In general, the I/O is improved by one to two orders of magnitude over real-world datasets using up to 1152 CPUmore »cores. Spatial Join query is used as an exemplar to demonstrate an end-to-end application using MPI-Vector-IO.« less
2. Spatial Data Decomposition and Load Balancing on HPC Platforms

   https://doi.org/10.1145/3332186.3333266  

   Yang, Jie ; Paudel, Anmol ; Puri, Satish ( July 2019 , Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning))

   We are in the era of Spatial Big Data. Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity and heterogeneity. For example, OpenStreetMap data for the whole world is about 1 TB and NASA world climate datasets are about 17 TB. Spatial data volume and variety makes spatial computations both data-intensive and compute-intensive. Due to the irregular distribution of spatial data, domain decomposition becomes challenging. In this work, we present spatial data partitioning technique that takes into account spatial join cost. In addition, we present spatial join computation using Asynchronous Dynamic Load Balancing (ADLB) library. ADLB is a software library designed to help rapidly build scalable parallel programs using MPI. We evaluated the performance of ADLB-based MPI-GIS implementation. In our existing work, spatial data movement cost from ADLB server to worker MPI processes limited the scalability of MPI-GIS.
3. Hierarchical Filter and Refinement System Over Large Polygonal Datasets on CPU-GPU

   https://doi.org/10.1109/HiPC.2019.00027  

   Liu, Yiming ; Yang, Jie ; Puri, Satish ( December 2019 , 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC))

   In this paper, we introduce our hierarchical filter and refinement technique that we have developed for parallel geometric intersection operations involving large polygons and polylines. The inputs are two layers of large polygonal datasets and the computations are spatial intersection on a pair of cross-layer polygons. These intersections are the compute-intensive spatial data analytic kernels in spatial join and map overlay computations. We have extended the classical filter and refine algorithms using PolySketch Filter to improve the performance of geospatial computations. In addition to filtering polygons by their Minimum Bounding Rectangle (MBR), our hierarchical approach explores further filtering using tiles (smaller MBRs) to increase the effectiveness of filtering and decrease the computational workload in the refinement phase. We have implemented this filter and refine system on CPU and GPU by using OpenMP and OpenACC. After using R-tree, on average, our filter technique can still discard 69% of polygon pairs which do not have segment intersection points. PolySketch filter reduces on average 99.77% of the workload of finding line segment intersections. PNP based task reduction and Striping algorithms filter out on average 95.84% of the workload of Point-in-Polygon tests. Our CPU-GPU system performs spatial join on two shapefiles, namely USA Watermore »Bodies and USA Block Group Boundaries with 683K polygons in about 10 seconds using NVidia Titan V and Titan Xp GPU.« less
4. Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

   https://doi.org/10.1007/s41019-020-00145-x  

   Gallet, Benoit ; Gowanlock, Michael ( March 2021 , Data Science and Engineering)

   Abstract Given two datasets (or tables) A and B and a search distance $$\epsilon$$ ϵ , the distance similarity join, denoted as $$A \ltimes _\epsilon B$$ A ⋉ ϵ B , finds the pairs of points ( $$p_a$$ p a , $$p_b$$ p b ), where $$p_a \in A$$ p a ∈ A and $$p_b \in B$$ p b ∈ B , and such that the distance between $$p_a$$ p a and $$p_b$$ p b is $$\le \epsilon$$ ≤ ϵ . If $$A = B$$ A = B , then the similarity join is equivalent to a similarity self-join, denoted as $$A \bowtie _\epsilon A$$ A ⋈ ϵ A . We propose in this paper Heterogeneous Epsilon Grid Joins ( HEGJoin ), a heterogeneous CPU-GPU distance similarity join algorithm. Efficiently partitioning the work between the CPU and the GPU is a challenge. Indeed, the work partitioning strategy needs to consider the different characteristics and computational throughput of the processors (CPU and GPU), as well as the data-dependent nature of the similarity join that accounts in the overall execution time (e.g., the number of queries, their distribution, the dimensionality, etc.). In addition to HEGJoin , we design in this paper amore »dynamic and two static work partitioning strategies. We also propose a performance model for each static partitioning strategy to perform the distribution of the work between the processors. We evaluate the performance of all three partitioning methods by considering the execution time and the load imbalance between the CPU and GPU as performance metrics. HEGJoin achieves a speedup of up to $$5.46\times$$ 5.46 × ( $$3.97\times$$ 3.97 × ) over the GPU-only (CPU-only) algorithms on our first test platform and up to $$1.97\times$$ 1.97 × ( $$12.07\times$$ 12.07 × ) on our second test platform over the GPU-only (CPU-only) algorithms.« less
5. HEALS: A Parallel eALS Recommendation System on CPU/GPU Heterogeneous Platforms

   https://doi.org/10.1109/HiPC53243.2021.00039  

   Wang, Qihan ; Niu, Wei ; Chen, Li ; Jin, Ruoming ; Ren, Bin ( December 2021 , 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC))

   Alternating Least Square (ALS) is a classic algorithm to solve matrix factorization widely used in recommendation systems. Existing efforts focus on parallelizing ALS on multi-/many-core platforms to handle large datasets. Recently, an optimized ALS variant called eALS was proposed, and it yields significantly lower time complexity and higher recommending accuracy than ALS. However, it is challenging to parallelize eALS on modern parallel architectures (e.g., CPUs and GPUs) because: 1) eALS’ data dependence prevents it from fine-grained parallel execution, thus eALS cannot fully utilize GPU's massive parallelism, 2) the sparsity of input data causes poor data locality and unbalanced workload, and 3) its large memory usage cannot fit into GPU's limited on-device memory, particularly for real-world large datasets. This paper proposes an efficient CPU/GPU heterogeneous recommendation system based on fast eALS for the first time (called HEALS) that consists of a set of system optimizations. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. HEALS also presents a CPU/GPU collaboration model that can explore both task parallelism and data parallelism. HEALS also optimizes this collaboration model with data communication overlapping and dynamic workload partition between CPU and GPU. Moreover, HEALS ismore »further enhanced by various parallel techniques (e.g., loop unrolling, vectorization, and GPU parallel reduction). Evaluation results show that HEALS outperforms other state-of-the-art baselines in both performance and recommendation quality. Particularly, HEALS achieves up to 5.75 x better performance than a state-of-the-art ALS GPU library. This work also demonstrates the possibility of conducting fast recommendations on large datasets with constrained (or relaxed) hardware resources, e.g, a single CPU/GPU node.« less