Cost-effective and scalable video analytics are essential for precision livestock monitoring, where high-resolution footage and near-real-time monitoring needs from commercial farms generates substantial computational workloads. This paper presents a comprehensive case study on optimizing a poultry welfare monitoring system through system-level improvements across detection, tracking, clustering, and behavioral analysis modules. We introduce a set of optimizations, including multi-level parallelization, Optimizing code with substituting CPU code with GPU-accelerated code, vectorized clustering, and memory-efficient post-processing. Evaluated on real-world farm video footage, these changes deliver up to a 2 × speedup across pipelines without compromising model accuracy. Our findings highlight practical strategies for building high-throughput, low-latency video inference systems that reduce infrastructure demands in agricultural and smart sensing deployments as well as other large-scale video analytics applications.

Whereas the literature describes an increasing number of graph algorithms, loading graphs remains a time-consuming component of the end-to-end execution time. Graph frameworks often rely on custom graph storage formats, that are not optimized for efficient loading of large-scale graph datasets. Furthermore, graph loading is often not optimized as it is time-consuming to implement. This shows a demand for high-performance libraries capable of efficiently loading graphs to (i) accelerate designing new graph algorithms, (ii) to evaluate the contributions across a wide range of graph datasets, and (iii) to facilitate easy and fast comparisons across different graph frameworks. We present ParaGrapher, a library for loading large-scale compressed graphs in parallel and distributed graph frameworks. ParaGrapher supports (a) loading the graph while the caller is blocked and (b) interleaving graph loading with graph processing. ParaGrapher is designed to support loading graphs in shared-memory, distributed-memory, and out-of-core graph processing. We explain the design of ParaGrapher and present a performance model of graph decompression. Our evaluation shows that ParaGrapher delivers up to 3.2 times speedup in loading and up to 5.2 times speedup in end-to-end execution (i.e., through interleaved loading and execution).

Progress in High-Performance Computing in general, and High-Performance Graph Processing in particular, is highly dependent on the availability of publicly-accessible, relevant, and realistic data sets. To ensure continuation of this progress, we (i) investigate and optimize the process of generating large sequence similarity graphs as an HPC challenge and (ii) demonstrate this process in creating MS-BioGraphs, a new family of publicly available real-world edge-weighted graph datasets with up to 2.5 trillion edges, that is, 6.6 times greater than the largest graph published recently. The largest graph is created by matching (i.e., all-toall similarity aligning) 1.7 billion protein sequences. The MSBioGraphs family includes also seven subgraphs with different sizes and direction types. We describe two main challenges we faced in generating large graph datasets and our solutions, that are, (i) optimizing data structures and algorithms for this multi-step process and (ii) WebGraph parallel compression technique. The datasets are available online on https://blogs.qub.ac.uk/ DIPSA/MS-BioGraphs.

Dengue Fever (DF) is an emerging mosquito-borne infectious disease that affects hundred of millions of people each year with considerable morbidity and mortality rates, especially for children. Together with global climate changes, it is continuously increasing in terms of number of cases and new locations. Thus, having effective early warning systems becomes an urgent need to improve disease controls and prevention. In this paper, we introduce a novel framework, called Proximity Time Ensemble, to predict DF outbreaks for multiple areas (provinces) and multiple time steps ahead, and to study the effects of climate data on DF outbreaks. PT-Ensem consists of 6 key components: (1) an event-to-event probabilistic framework to study links among extreme climate events and DF outbreaks; (2) a proximity graph that connects similar provinces; (3) an ensemble prediction technique that combines many different advanced machine learning (ML) methods to predict outbreaks within t time steps in the future using extreme climate events as model inputs; (4) a data aggregate scheme to enrich training data for each province via its neighbors in the proximity graph; (5) a proximity propagation step that propagates predicted results among similar provinces via the proximity graph until maximal agreements are reached among provinces; and (6) a time propagation step to propagate results via different predicted time steps in each province. We use PT-Ensem to predict DF outbreaks for all provinces in Vietnam using data collected from 1997-2016. Experiments show that PT-Ensem acquires significant performance boost compared to many highly-rated ML models like XGBoost, LightGBM and Catboost in the outbreak prediction task. Compared to most recent deep learning approaches like LSTM-ATT, LSTM, CNN and Transformer for predicting DF incidence, PT-Ensem also dominates in both prediction accuracy and computation times.

This paper presents a novel map-reduce runtime system that is designed for scalability and for composition with other parallel software. We use a modified programming interface that expresses reduction operations over data containers as opposed to key-value pairs. This design choice admits higher efficiency as the programmer can select appropriate data structures. Our runtime targets shared memory systems, which are increasingly capable of performing data analytics on terabyte-sized data sets stored in-memory. Our map-reduce runtime is built over the Cilk programming language and outperforms Phoenix++, by 1.5x-4x for 5 out of 7 map-reduce benchmarks on 48 threads. These results arise from a combination of factors: (i) the reduction of framework overheads, including the elimination of repeated (de-)serialization of key-value pairs; (ii) the use of more appropriate intermediate data structures that reductions over containers support.

One of the main targets of data analytics is unstructured data, which primarily involves textual data. High-performance processing of textual data is non-trivial. We present the HPTA library for high-performance text analytics. The library helps programmers to map textual data to a dense numeric representation, which can be handled more efficiently. HPTA encapsulates three performance optimizations: (i) efficient memory management for textual data, (ii) parallel computation on associative data structures that map text to values and (iii) optimization of the type of associative data structure depending on the program context. We demonstrate that HPTA outperforms popular frameworks for text analytics such as scikit-learn.

Increasingly large amounts of data are stored in main memory of data center servers. However, DRAM-based memory is an important consumer of energy and is unlikely to scale in the future. Various byte-addressable non-volatile memory (NVM) technologies promise high density and near-zero static energy, however they suffer from increased latency and increased dynamic energy consumption.