Halide perovskites have emerged as promising candidates for various applications, such as photovoltaic, optoelectronic and thermoelectric applications. The knowledge of the thermal transport of halide perovskites is essential for enhancing the device performance for these applications and improving the understanding of heat transport in complicated material systems with atomic disorders. In this work, the current understanding of the experimentally and theoretically obtained thermal transport properties of halide perovskites is reviewed. This study comprehensively examines the reported thermal conductivity of methylammonium lead iodide, which is a prototype material, and provides theoretical frameworks for its lattice vibrational properties. The frameworks and discussions are extended to other halide perovskites and derivative structures. The implications for device applications, such as solar cells and thermoelectrics, are discussed.
A computational storage device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge: cost-effective virtualization . This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational storage devices, and (3) how to provide them to users. However, the existing methods for computational storage virtualization severely suffer from their low performance and high costs due to the lack of hardware-assisted virtualization support. In this work, we propose SmartFVM-Engine , an FPGA card designed to maximize the performance and cost-effectiveness of computational storage virtualization. SmartFVM-Engine introduces three key ideas to achieve the design goals. First, it achieves high virtualization performance by applying hardware-assisted virtualization to both computation and storage units. Second, it further improves the performance by applying hardware-assisted resource orchestration for the virtualized units. Third, it achieves high cost-effectiveness by dynamically constructing and scheduling virtual computational storage devices. To the best of our knowledge, this is the first work to implement a hardware-assisted virtualization mechanism for modern computational storage devices.
Modern servers are actively deploying Solid-State Drives (SSDs) thanks to their high throughput and low latency. However, current server architects cannot achieve the full performance potential of commodity SSDs, as SSDs are complex devices designed for specific goals (e.g., latency, throughput, endurance, cost) with their internal mechanisms undisclosed to users. In this article, we propose SSDcheck , a novel SSD performance model to extract various internal mechanisms and predict the latency of next access to commodity black-box SSDs. We identify key performance-critical features (e.g., garbage collection, write buffering) and find their parameters (i.e., size, threshold) from each SSD by using our novel diagnosis code snippets. Then, SSDcheck constructs a performance model for a target SSD and dynamically manages the model to predict the latency of the next access. In addition, SSDcheck extracts and provides other useful internal mechanisms (e.g., fetch unit in multi-queue SSDs, background tasks triggering idle-time interval) for the storage system to fully exploit SSDs. By using those useful features and the performance model, we propose multiple practical use cases. Our evaluations show that SSDcheck’s performance model is highly accurate, and proposed use cases achieve significant performance improvement in various scenarios.
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