One table to count them all: parallel frequency estimation on single-board computers

Sketches are probabilistic data structures that can provide approx- imate results within mathematically proven error bounds while using orders of magnitude less memory than traditional approaches. They are tailored for streaming data analysis on architectures even with limited memory such as single-board computers that are widely exploited for IoT and edge computing. Since these devices offer multiple cores, with efficient parallel sketching schemes, they are able to manage high volumes of data streams. However, since their caches are relatively small, a careful parallelization is required. In this work, we focus on the frequency estimation problem and evaluate the performance of a high-end server, a 4-core Raspberry Pi and an 8-core Odroid. As a sketch, we employed the widely used Count-Min Sketch. To hash the stream in parallel and in a cache-friendly way, we applied a novel tabulation approach and rearranged the auxiliary tables into a single one. To parallelize the process with performance, we modified the workflow and applied a form of buffering between hash computations and sketch updates. Today, many single-board computers have heterogeneous processors in which slow and fast cores are equipped together. To utilize all these cores to their full potential, we proposed a dynamic load-balancing mechanism which signif- icantly increased the performance of frequency estimation

Supertwin: Digital twins for high-performance computing clusters

Computational systems are extremely complex and the composition of their hardware and software components greatly vary from machine to machine. This nonstandardized environment can cause up to 100% difference between the best and worst completion times with the same input data. On top of that, the shape of the input data and executed kernels add even more variance to the situation. However, computational systems are not completely hostile environments. These systems are also equipped with diverse observability capabilities. A typical Linux system can report thousands of real-time execution and performance-related metrics from both its hardware and software components. Digital Twins are knowledge management systems that have vast application areas in the industry, however, digital twins of computational systems remain a gap in the literature. SuperTwin is a knowledge representation generator and manager of the tools and performance data that interact with it. It creates a digital twin of a computational system via detailed probing, configures and listens to performance metric samplers, creates real-time visualizations, links the acquired information, and enables semantic queries for advanced analysis. In this work, design and implementation choices for SuperTwin are thoroughly presented. The effect of profiling on remote systems is analyzed and the accuracy of the readings is investigated

Streaming Hypergraph Partitioning Algorithms on Limited Memory Environments

International audienceMany well-known, real-world problems involve dynamic, interrelated data items. Hypergraphs are powerful combinatorial structures that are frequently used to model such data. Many of today's data-centric have streaming data; new items arrive continuously, and the data grow over time. With paradigms such as Internet of Things and Edge Computing, such applications become more natural and more practical. In this work, we assume a streaming model where the data items and their relations are modeled as a hypergraph, which is generated at the edge. This hypergraph is then partitioned, and the parts are sent to remote nodes via an algorithm running on a memory-restricted device, such as a single board computer. Such a partitioning is usually performed by taking a connectivity metric into account to minimize the communication cost of later analyses that will be performed in a distributed fashion. Although there are many offline tools that can partition static hypergraphs effectively, algorithms for the streaming settings are rare. We analyze a well-known algorithm from the literature and significantly improve its run time by altering its inner data structure. On a medium-scale hypergraph, the new algorithm reduces the run time from 17800 seconds to 10 seconds. We then propose sketch-and hash-based algorithms, as well as ones that can leverage extra memory to store a small portion of the data to enable the refinement of partitioning when possible. We experimentally analyze the performance of these algorithms and report their run times, connectivity metric scores, and memory uses on a high-end server and four different single-board computer architectures

Machine learning-based load distribution and balancing in heterogeneous database management systems

For dynamic and continuous data analysis, conventional OLTP systems are slow in performance. Today's cutting-edge high-performance computing hardware, such as GPUs, has been used as accelerators for data analysis tasks, which traditionally leverage CPUs on classical database management systems (DBMS). When CPUs and GPUs are used together, the architectural heterogeneity, that is, leveraging hardware with different performance characteristics jointly, creates complex problems that need careful treatment for performance optimization. Load distribution and balancing are crucial problems for DBMSs working on heterogeneous architectures. In this work, focusing on a hybrid, CPU-GPU database management system to process users' queries, we propose heuristical and machine-learning-based (ML-based) load distribution and balancing models. In more detail, we employ multiple linear regression (MLR), random forest (RF), and Adaboost (Ada) models to dynamically decide the processing unit for each incoming query based on the response time predictions on both CPU and GPU. The ML-based models outperformed the other algorithms, as well as the CPU and GPU-only running modes with up to 27%, 29%, and 40%, respectively, in overall performance (response time) while answering intense real-life working scenarios. Finally, we propose to use a hybrid load-balancing model that would be more efficient than the models we tested in this work

The Distribution of Hepatitis C Virus (HCV) Genotypes in 59 HCV Infected Patients: A Multicenter Study

Eighty anti-HCV positive serum samples were collected at university centers in Malatya, Erzurum, Samsun and Konya. HCV-RNA sequences were detected in 59 (73.3%) of these samples by reverse transcriptase-polymerase chain reaction (RT-PCR) using primers from the 5’ non-coding region. HCV-RNA positive samples were subsequently genotyped using type-specific primers from the core region of the virus. Type II (I b) infection was detected in 41 samples (69.5%), while a mix type I (I a) and II (I b) infection were found in another 3 samples (5.1%). The remaining 15 samples (25.4%) could have not been typed. These results together with the results of previous studies suggest the predominance of genotype II infection in Turkish patients which is known to have poorer prognosis and lower responce to interferon treatment