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1. Exploring the Potential of Next Generation Software-Defined In-Memory Frameworks

   https://doi.org/10.1109/SBAC-PAD.2018.00042  

   Chen, Shouwei; Rodero, Ivan (January 2018, 2018 30th International Symposium on Computer Architecture and High Performance Computing)

   As in-memory data analytics become increasingly important in a wide range of domains, the ability to develop large-scale and sustainable platforms faces significant challenges related to storage latency and memory size constraints. These challenges can be resolved by adopting new and effective formulations and novel architectures such as software-defined infrastructure. This paper investigates the key issue of data persistency for in-memory processing systems by evaluating persistence methods using different storage and memory devices for Apache Spark and the use of Alluxio. It also proposes and evaluates via simulation a Spark execution model for using disaggregated off- rack memory and non-volatile memory targeting next-generation software-defined infrastructure. Experimental results provide better understanding of behaviors and requirements for improving data persistence in current in-memory systems and provide data points to better understand requirements and design choices for next-generation software-defined infrastructure. The findings indicate that in-memory processing systems can benefit from ongoing software-defined infrastructure implementations; however current frameworks need to be enhanced appropriately to run efficiently at scale. 

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2. High-Level Synthesis Based FPGA Hardware Architecture for PCA+SVM for Real-Time Processing on Edge Computing Platforms

   https://doi.org/10.1109/ACCESS.2025.3645763  

   Mohsin, Mokhles A; Perera, Darshika G (January 2025, IEEE Access)

   In the IoT and smart systems era, the massive amount of data generated from various IoT and smart devices are often sent directly to the cloud infrastructure for processing, analyzing, and storing. While handling this big data, conventional cloud infrastructure encounters many challenges, e.g., scarce bandwidth, high latency, real-time constraints, high power, and privacy issues. The edge-centric computing is transpiring as a synergistic solution to address these issues of cloud computing, by enabling processing/analyzing the data closer to the source of the data or at the network’s edge. This in turn allows real-time and in-situ data analytics and processing, which is imperative for many real-world IoT and smart systems, such as smart cars. Since the edge computing is still in its infancy, innovative solutions, models, and techniques are needed to support real-time and in-situ data processing and analysis of edge computing platforms. In this research work, we introduce a novel, unique, and efficient FPGA-HLS-based hardware accelerator for PCA+SVM model for real-time processing and analysis on edge computing platforms. This is inspired by our previous work on PCA+SVM models for edge computing applications. It was demonstrated that the amalgamation of principal component analysis (PCA) and support vector machines (SVM) leads to high classification accuracy in many fields. Also, machine learning techniques, such as SVM, can be utilized for many edge tasks, e.g. anomaly detection, health monitoring, etc.; and dimensionality reduction techniques, such as PCA, are often used to reduce the data size, which in turn vital for memory-constrained edge devices/platforms. Furthermore, our previous works demonstrated that FPGA’s many traits, including parallel processing abilities, low latency, and stable throughput despite the workload, make FPGAs suitable for real-time processing of edge computing applications/platforms. Our proposed FPGA-HLS-based PCA+SVM hardware IP achieves up to 254x speedup compared to its embedded software counterpart, while maintaining small area and low power requirements of edge computing applications. Our experimental results show great potential in utilizing FPGA-based architectures to support real-time processing on edge computing applications. 

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3. OpenMSIStream: A Python package for facilitatingintegration of streaming data in diverse laboratory environments

   https://doi.org/10.21105/joss.04896  

   Eminizer, Margaret; Tabrisky, Sam; Sharifzadeh, Amir; DiMarco, Christopher; Diamond, Jacob M.; Ramesh, K. T.; Hufnagel, Todd C.; McQueen, Tyrel M.; Elbert, David (March 2023, Journal of Open Source Software)

   OpenMSIStream provides seamless connection of scientific data stores with streaming infrastructure to allow researchers to leverage the power of decoupled, real-time data streaming architectures. Data streaming is the process of transmitting, ingesting, and processing data continuously rather than in batches. Access to streaming data has revolutionized many industries in the past decade and created entirely new standards of practice and types of analytics. While not yet commonly used in scientific research, data streaming has the potential to become a key technology to drive rapid advances in scientific data collection (e.g., Brookhaven National Lab (2022)). This paucity of streaming infrastructures linking complex scientific systems is due to a lack of tools that facilitate streaming in the diverse and distributed systems common in modern research. OpenMSIStream closes this gap between underlying streaming systems and common scientific infrastructure. Closing this gap empowers novel streaming applications for scientific data including automation of data curation, reduction, and analysis; real-time experiment monitoring and control; and flexible deployment of AI/ML to guide autonomous research. Streaming data generally refers to data continuously generated from multiple sources and passed in small packets (termed messages). Streaming data messages are typically organized in groups called topics and persist for periods of time conducive to processing for multiple uses either sequentially or in small groups. The resulting flows of raw data, metadata, and processing results form “ecosystems” that automate varied data-driven tasks. A strength of data streaming ecosystems is the use of publish-subscribe (“pub/sub”) messaging backbones that decouple data senders (publishers) and recipients (subscribers). Popular message-focused middleware solutions such as RabbitMQ (VMware, 2022), Apache Pulsar (Apache Software Foundation, 2022b), and Apache Kafka (Apache Software Foundation, 2022a) all provide differing capabilities as backbones. OpenMSIStream provides robust and efficient, yet easy, access to the rich data streaming systems of Apache Kafka. 

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4. A Survey of Resource Management for Processing-In-Memory and Near-Memory Processing Architectures

   https://doi.org/10.3390/jlpea10040030  

   Khan, Kamil; Pasricha, Sudeep; Kim, Ryan Gary (December 2020, Journal of Low Power Electronics and Applications)

   null (Ed.)

   Due to the amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become a bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D-stacked memories or charge-sharing-based bitwise operations in dynamic random-access memory (DRAM). However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management. 

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5. Data Management: Interactions with Computer Architecture and Systems

   https://doi.org/10.1017/9781009122115  

   Zhang, Xiaodong; Lee, Rubao (November 2024, Cambridge University Press)

   This guide illuminates the intricate relationship between data management, computer architecture, and system software. It traces the evolution of computing to today's data-centric focus and underscores the importance of hardware-software co-design in achieving efficient data processing systems with high throughput and low latency. The thorough coverage includes topics such as logical data formats, memory architecture, GPU programming, and the innovative use of ray tracing in computational tasks. Special emphasis is placed on minimizing data movement within memory hierarchies and optimizing data storage and retrieval. Tailored for professionals and students in computer science, this book combines theoretical foundations with practical applications, making it an indispensable resource for anyone wanting to master the synergies between data management and computing infrastructure. 

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