Thanks to the advance of device scaling technologies, the capacity of SSDs is rapidly increasing. Such increase, however, comes at the cost of a huge index table requiring large DRAM. To provide reasonable performance with less DRAM, various index structures exploiting locality and regularity of I/O references have been proposed. However, they provide deteriorated performance depending on I/O patterns and storage fragmentation. This paper proposes a novel approximate index structure, called AppL, which combines memory-efficient approximate indices and an LSM-tree that has an append-only and sorted nature. AppL reduces the index size to 6-8-bits per entry, which is considerably smaller than the typical index structures requiring 32-64-bits, and maintains such high memory efficiency irrespective of locality and fragmentation. By alleviating memory pressure, AppL achieves 33.6-72.4% shorter read latency and 28.4%-83.4% higher I/O throughput than state-of-the-art techniques.

The expansion of memory disaggregation, driven by data-centric applications, increases heterogeneity in memory systems. This shift enables the use of inexpensive, yet lifetime-limited, flash memory to be used as a memory expansion module. We argue that TRIM should be introduced into memory management systems to effectively respond to this transition. In this position paper, we explore the potential adoption of flash memory as memory expansion and present an analytical model that offers a straightforward yet rigorous evaluation of TRIM's effectiveness. Using this model and characteristics extracted from real-world workloads, we evaluate the effectiveness of TRIM in scalable memory systems and prove its necessity.

Android, with its vast global adoption and diverse hardware ecosystem, poses unique challenges for performance benchmarking, particularly from a user-centric perspective. Traditional benchmarks often fail to capture the intricacies of userperceived performance, relying on component-level metrics or synthetic workloads that do not reflect real-world usage. This paper proposes Real-Time User-Experience, RTUX, a novel benchmarking tool designed to measure Android system performance as perceived by users. RTUX employs external camera-based GUI state recognition and scenario-based testing to evaluate app loadtimes and in-app transitions under diverse conditions. Using CNN models and a unique system structure, RTUX reliably replays human-like interactions, enabling repeatable and robust performance assessments. Through experiments with 100 scenario repetitions involving popular Android apps, we uncover some system bottlenecks, such as suboptimal writeback configurations and I/O scheduler inefficiencies. The tool demonstrates how targeted optimizations can yield tangible improvements in user experience.

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnetDTI) and for neurite orientation dispersion and density imaging (DIFFnetNODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.