AdaBoost Parallelization on PC Clusters with Virtual Shared Memory for Fast Feature Selection

Performance analysis of SPMD algorithms on a network of workstations with virtual shared memory

Feature selection filter for classification of power system operating states

This paper introduces spinning-on-coherency (SOC) a technique for virtual shared memory (VSM) which enables latency-hiding of remote reads and the removal of related synchronisation points. Coherence-bits are hardware-tags associated with addresses which record local access permissions (such as read, write, invalid). In SOC a user-thread spins on the particular coherence-bits associated with an address until the new data value is asynchronously propagated and the address becomes valid. Data-propagation occurs when another node issues an update after having written the new value. Performance improvements are demonstrated for two codes, representing the core communication found in Shallow (a well known numerical weather prediction benchmark), and CG (from the NAS Parallel Benchmarks). These are run on a 30 node prototype distributed memory architecture (EDS), with invalidation based sequentially consistent VSM. SOC is also applicable to other consistency models and directory schemes, whether in hardware or software and complements other VSM optimisations. Currently such optimisation is performed by the programmer, but there is much scope for automating this process within a compiler.

Recent advances in unsteady flow visualization

Offloading footwear is a critical intervention for preventing and treating diabetic foot ulcers (DFUs), as it reduces plantar pressure (PP) and promotes healing. However, decision-making around its prescription remains fragmented, with variability in feature selection, limited personalization, and diverse evaluation approaches. While guidelines, knowledge-based systems, and machine learning (ML) applications have explored elements of decision-making, no clinical decision support system (CDSS) currently exists to provide feature-level prescriptions. We conducted a narrative review of 45 studies, including 12 guidelines/protocols, 25 knowledge-based systems, and eight ML applications. Studies published from inception to August 2025 were included. Studies were thematically analyzed by type of knowledge, decision logic, evaluation methods, and associated technologies. Guidelines emphasize PP thresholds (≤200 kPa or ≥25–30% reduction) but lack detailed outputs. Knowledge-based systems apply rule-based and sensor-driven logic, integrating PP monitoring, adherence tracking, and usability testing. ML applications introduce predictive classification, optimization, and generative models, achieving high computational accuracy but with limited explainability and clinical validation. Evaluation practices remain fragmented: biomechanical testing dominates protocols, usability and adherence assessments are common in knowledge-based systems, while ML studies emphasize technical accuracy without linking to long-term clinical outcomes. From this synthesis, we propose a five-component framework for CDSS development that discusses: (1) minimum viable dataset, (2) hybrid decision architecture combining rules, optimization, and explainable ML, (3) structured feature-level outputs, (4) continuous validation and evaluation, and (5) integration into clinical and telehealth workflows. The proposed framework presents a pathway to transform fragmented approaches into scalable, patient-centered CDSSs. Prioritizing interoperable datasets, explainable models, and outcome-driven evaluation will be critical for clinical adoption and improved DFU care.

Increasingly many applications of machine learning are encountering large data that were almost unimaginable just a few years ago, and hence, many of the current algorithms cannot handle, i.e., do not scale to, today's extremely large volumes of data. The data are made up of a large set of features describing each observation, and the complexity of the models for making predictions tend to increase not only with the number of observations, but also the number of features. Fortunately, not all of the features that make up the data carry meaningful information about making the predictions. Thus irrelevant features should be filtered from the data prior to building a model. Such a process of removing features to produce a subset is commonly referred to as feature subset selection. In this work, we present two new filter-based feature subset selection algorithms that are scalable to large data sets that address: (i) potentially large & distributed data sets, and (ii) they are capable of scaling to very large feature sets. Our first proposed algorithm, Neyman-Pearson Feature Selection (NPFS), uses a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any feature selection algorithm, regardless of the feature selection criteria used, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point, and it fits into a computationally attractive MapReduce model. We also describe a sequential learning framework for feature subset selection (SLSS) that scales with both the number of features as well as the number of observations. SLSS uses bandit algorithms to process features and form a level of importance for each feature. Feature selection is performed independently from the optimization of any classifier to reduce unnecessary complexity. We demonstrate the capabilities of NPFS and SLSS on synthetic and real-world data sets. We also present a new approach for classifier-dependent feature selection that is an online learning algorithm that easily handles large amounts of missing feature values in a data stream. There are many real-world applications that can benefit from scalable feature subset selection algorithms; one such area is the study of the microbiome (i.e., the study of micro-organisms and their influence on the environments that they inhabit). Feature subset selection algorithms can be used to sift through massive amounts of data collected from the genomic sciences to help microbial ecologists understand the microbes - particularly the micro-organisms that are the best indicators by some phenotype, such as healthy or unhealthy. In this work, we provide insights into data collected from the American Gut Project, and deliver open-source software implementations for feature selection with biological data formats.

Investigates the distributed shared virtual memory (DSVM) technique in a distributed diskless PC cluster system environment. By integrating distributed shared memory (DSM) and virtual memory functions, the DSVM model not only supports the sharing of process space but also provides the sharing of system-supported space (e.g. swapping area). DSVM controllers are handlers in a diskless PC cluster system whose responsibility is to control the operations of DSM and to handle the sharing when the local physical memory needs to do page/process swap-in and out. This paper presents the model, describes the requirements and how the model supports the diskless PC in a diskless PC cluster system, discusses several design issues, and narrates prototype implementation details and considerations. The developed primitives of this prototype are the base of a complete DSVM mechanism and process migration. Integrating the two mechanisms will promote the system to be a powerful group computing platform.

Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.

With the advancement of industrial digitization, utilizing large datasets for model training to boost performance is a pivotal technical approach for industry progress. However, raw training datasets often contain abundant redundant features, which increase model training's computational cost and impair generalization ability. To tackle this, this study proposes the bionic ABCCOA algorithm, an enhanced version of the bionic Coati Optimization Algorithm (COA), to improve redundant feature elimination in datasets. To address the bionic COA's inadequate global search performance in feature selection (FS) problems, leading to lower classification accuracy, an adaptive search strategy is introduced. This strategy combines individual learning capability and the learnability of disparities, enhancing global exploration. For the imbalance between the exploration and exploitation phases in the bionic COA algorithm when solving FS problems, which often traps it in suboptimal feature subsets, a balancing factor is proposed. By integrating phase control and dynamic adjustability, a good balance between the two phases is achieved, reducing the likelihood of getting stuck in suboptimal subsets. Additionally, to counter the bionic COA's insufficient local exploitation performance in FS problems, increasing classification error rates, a centroid guidance strategy is presented. By combining population centroid guidance and fractional-order historical memory, the algorithm lowers the classification error rate of feature subsets and speeds up convergence. The bionic ABCCOA algorithm was tested on the CEC2020 test functions and engineering problem, achieving an over 90% optimization success rate and faster convergence, confirming its efficiency. Applied to 27 FS problems, it outperformed comparative algorithms in best, average, and worst fitness function values, classification accuracy, feature subset size, and running time, proving it an efficient and robust FS algorithm.

Introduction Large language models (LLMs) have the potential to realize accurate risk stratification and disease prediction by integrating multimodal data, such as electronic health records, medical images, and genomic profiles. However, in complex tasks like atherosclerosis risk estimation, the design of the prompt is critical to bring out the performance of LLMs. Several researches have demonstrated that in-context learning, where the prompt contains some cases, improves the performance of LLMs when the contained cases are carefully selected. Because manual selection usually requires enormous costs, automatic case selection has been desired. Methods This study aims to improve the effectiveness of in-context learning in LLM for atherosclerosis risk prediction by introducing a strategy for case selection. Our method involves two key components: feature selection and case selection. In the feature selection phase, we compute the mutual information between the clinical features and atherosclerosis risk using the database to extract the essential clinical features. This feature selection removes the unimportant features that disturb the performance of the case selection mechanism. Then, in the case selection phase, we select several cases from the database that are the most similar to the patient being diagnosed. This study compares three metrics to compute the similarity score: - Mahalanobis distance (trained via large margin nearest neighbor classification; LMNN [1]) - Cosine similarity (between the vector representations in KNN-augmented in-context example selection; KATE [2]) - Euclidean distance (between the prompt features) This combined strategy supplies the LLM with informative cases and thereby enhances the diagnostic performance. Results We used a fine-tuned Llama3-8B model for our target language and analyzed a dataset of 117,709 cases. Each case contained 96 features that were collected from annual health check-up data. From this dataset, 1,000 cases each were randomly selected for validation and test datasets. The remaining data were used as the database in our system. We considered a binary classification task and assigned the positive label when the Cardio-Ankle Vascular Index (CAVI) was greater than 8.0. The proportion of selected features was adjusted in 10% increments to maximize the F1 score on the validation dataset. The result shows that our proposed method, the strategic feature and case selections, significantly improved the F1 score for the test dataset by around 2% compared with zero-shot prompt and random selection (see the attached table). Among three similarity metrics, Euclidean distance yielded the highest F1 scores. We also observed that the feature selection phase with the adjusted number of selected features improved the F1 score in almost all settings. Conclusion Our strategy for feature and case selections improved the performance of in-context learning for LLM-based atherosclerosis risk prediction.

NestStep is a parallel programming language for the BSP (bulk–synchronous–parallel) model of parallel computation. Extending the classical BSP model, NestStep supports dynamically nested parallelism by nesting of supersteps and a hierarchical processor group concept. Furthermore, NestStep adds a virtual shared memory realization in software, where memory consistency is relaxed to superstep boundaries. Distribution of shared arrays is also supported. A prototype for a subset of NestStep has been implemented based on Java as sequential basis language. The prototype implementation is targeted to a set of Java Virtual Machines coupled by Java socket communication to a virtual parallel computer.

This report describes the use and implementation of DOLIB (Distributed Object Library), a library of routines that emulates global or virtual shared memory on Intel multiprocessor systems. Access to a distributed global array is through explicit calls to gather and scatter. Advantages of using DOLIB include: dynamic allocation and freeing of huge (gigabyte) distributed arrays, both C and FORTRAN callable interfaces, and the ability to mix shared-memory and message-passing programming models for ease of use and optimal performance. DOLIB is independent of language and compiler extensions and requires no special operating system support. DOLIB also supports automatic caching of read-only data for high performance. The virtual shared memory support provided in DOLIB is well suited for implementing Lagrangian particle tracking techniques. We have also used DOLIB to create DONIO (Distributed Object Network I/O Library), which obtains over a 10-fold improvement in disk I/O performance on the Intel Paragon.

Surface reconstruction is a common problem in computer graphics. Given a set of points sampled from some surface, a triangle mesh interpolating or approximating the points is to be obtained. One of very often used techniques for solving this problem is the selection of surface triangles from the set of Delaunay tetrahedronization faces. In the case of large data, it is difficult to obtain the tetrahedronization due to its huge memory requirements. One of possible solutions is to use distributed computing. Here, we describe the newly developed VSM (Virtual Shared Memory) distributed toolkit and its utilization for the task of surface reconstruction. By our approach, we were able to process dataset having 1.4M points on 4xP4 interconnected via 100Mb Ethernet in 6 hours. About 5GB of memory was consumed during the reconstruction.

This paper presents new compiler analysis for the elimination of invalidation traffic in virtual shared memory, using a hybrid distributed invalidation coherence scheme. The invalidation and acknowledgement messages are removed; this reduces both network invalidation traffic and the latency of a write fault. It aggressively exploits the SPMD execution model and uses array section analysis to accurately determine only those instances when invalidation is necessary, thus avoiding the additional read misses of previous schemes. Equations determining precisely what data should be invalidated are presented and translated into a form amenable to compiler analysis. Preliminary experimental results on a 30 node prototype architecture demonstrate the performance attainable using this scheme.

Feature selection is referred to the process of obtaining a subset from an original feature set according to certain feature selection criterion, which selects the relevant features of the dataset. It plays a role in compressing the data processing scale, where the redundant and irrelevant features are removed. Feature selection techniques show that more information is not always good in machine learning applications. Apply different algorithms for the data at hand and with baseline classification performance values we can select a final feature selection algorithm. In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and Machine learning as classifiers. The objective of this study is to select relevant features and analyze the outperform machine learning algorithms in order to train our model, predict and compare their classification performance. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. This proposed classifier model is applied to our smart meter datasets. To measure the performance of these selected features; seven benchmark classifier are used; Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbor (kNN), Naive Bayes (NB), Decision Tree (DT), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). This paper then analyzes the performance of all classifiers with feature selection in term of accuracy, sensitivity, F-Measure, Specificity, Precision, and MCC. From our experiment, we found that Random Forest classifier performed higher than other used classifiers.