Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Pakkausmenetelmät hajautetussa aikasarjatietokannassa

Rise of microservices and distributed applications in containerized deployments are putting increasing amount of burden to the monitoring systems. They push the storage requirements to provide suitable performance for large queries. In this paper we present the changes we made to our distributed time series database, Hawkular-Metrics, and how it stores data more effectively in the Cassandra. We show that using our methods provides significant space savings ranging from 50 to 90% reduction in storage usage, while reducing the query speeds by over 90\% compared to the nominal approach when using Cassandra. We also provide our unique algorithm modified from Gorilla compression algorithm that we use in our solution, which provides almost three times the throughput in compression with equal compression ratio.Hajautettujen järjestelmien yleistyminen on aiheuttanut valvontajärjestelmissä tiedon määrän kasvua, sillä aikasarjojen määrä on kasvanut ja niihin talletetaan useammin tietoa. Tämä on aiheuttanut kasvavaa kuormitusta levyjärjestelmille, joilla on ongelmia palvella kasvavia kyselyitä Tässä paperissa esittelemme muutoksia hajautettuun aikasarjatietokantaamme, Hawkular-Metricsiin, käyttäen hyödyksi tehokkaampaa tiedon pakkausta ja järjestelyä kun tietoa talletetaan Cassandraan. Nopeutimme kyselyjä lähes kymmenkertaisesti ja samalla pienensimme levytilavaatimuksia aineistosta riippuen 50-95%. Esittelemme myös muutoksemme Gorilla pakkausalgoritmiin, jota hyödynnämme tulosten saavuttamiseksi. Muutoksemme nopeuttavat pakkaamista melkein kolminkertaiseksi alkuperäiseen algoritmiin nähden ilman pakkaustehon laskua

An Intelligent Framework for Oversubscription Management in CPU-GPU Unified Memory

This paper proposes a novel intelligent framework for oversubscription management in CPU-GPU UVM. We analyze the current rule-based methods of GPU memory oversubscription with unified memory, and the current learning-based methods for other computer architectural components. We then identify the performance gap between the existing rule-based methods and the theoretical upper bound. We also identify the advantages of applying machine intelligence and the limitations of the existing learning-based methods. This paper proposes a novel intelligent framework for oversubscription management in CPU-GPU UVM. It consists of an access pattern classifier followed by a pattern-specific Transformer-based model using a novel loss function aiming for reducing page thrashing. A policy engine is designed to leverage the model's result to perform accurate page prefetching and pre-eviction. We evaluate our intelligent framework on a set of 11 memory-intensive benchmarks from popular benchmark suites. Our solution outperforms the state-of-the-art (SOTA) methods for oversubscription management, reducing the number of pages thrashed by 64.4\% under 125\% memory oversubscription compared to the baseline, while the SOTA method reduces the number of pages thrashed by 17.3\%. Our solution achieves an average IPC improvement of 1.52X under 125\% memory oversubscription, and our solution achieves an average IPC improvement of 3.66X under 150\% memory oversubscription. Our solution outperforms the existing learning-based methods for page address prediction, improving top-1 accuracy by 6.45\% (up to 41.2\%) on average for a single GPGPU workload, improving top-1 accuracy by 10.2\% (up to 30.2\%) on average for multiple concurrent GPGPU workloads.Comment: arXiv admin note: text overlap with arXiv:2203.1267