We introduce optimistic crash consistency, a new approach to crash consistency in journaling file systems. Using an array of novel techniques, we demonstrate how to build an optimistic commit protocol that correctly recovers from crashes and delivers high performance. We implement this optimistic approach within a Linux ext4 variant which we call OptFS. We introduce two new file-system primitives, osync() and dsync(), that decouple ordering of writes from their durability. We show through experiments that OptFS improves performance for many workloads, sometimes by an order of magnitude; we confirm its correctness through a series of robustness tests, showing it recovers to a consistent state after crashes. Finally, we show that osync() and dsync() are useful in atomic file system and database update scenarios, both improving performance and meeting application-level consistency demands.
Main Messages• Acute and chronic mesenteric ischaemia are morbid conditions challenging to diagnose.• MDCT is the first-line imaging test for evaluating patients with suspected mesenteric ischaemia.• Bowel findings include wall thickening, abnormal enhancement, pneumatosis and luminal dilation.• Vascular occlusion, portomesenteric venous gas, mesenteric congestion and free air can be seen.
Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline , i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored. This paper makes the following contributions: (1) We present the first comprehensive analysis of how the input data pipeline affects the training time of widely-used computer vision and audio Deep Neural Networks (DNNs), that typically involve complex data pre-processing. We analyze nine different models across three tasks and four datasets while varying factors such as the amount of memory, number of CPU threads, storage device, GPU generation etc on servers that are a part of a large production cluster at Microsoft. We find that in many cases, DNN training time is dominated by data stall time : time spent waiting for data to be fetched and pre-processed. (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls. (3) Finally, based on the insights from our analysis, we design and implement three simple but effective techniques in a data-loading library, CoorDL, to mitigate data stalls. Our experiments on a range of DNN tasks, models, datasets, and hardware configs show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5X on a single server).
e General Data Protection Regulation (GDPR) was introduced in Europe to o er new rights and protections to people concerning their personal data. We investigate GDPR from a database systems perspective, translating its legal articles into a set of capabilities and characteristics that compliant systems must support. Our analysis reveals the phenomenon of metadata explosion, wherein large quantities of metadata needs to be stored along with the personal data to satisfy the GDPR requirements. Our analysis also helps us identify the new workloads that must be supported under GDPR. We design and implement an open-source benchmark called GDPRbench that consists of workloads and metrics needed to understand and assess personal-data processing database systems. To gauge how ready the modern database systems are for GDPR, we modify Redis and PostgreSQL to be GDPR compliant. Our evaluations show that this modi cation degrades their performance by up to 5×. Our results also demonstrate that the current database systems are two to four orders of magnitude worse in supporting GDPR workloads compared to traditional workloads (such as YCSB), and also do not scale as the volume of personal data increases. We discuss the real-world implications of these ndings, and identify research challenges towards making GDPR compliance e cient in production environments. We release all of our so ware artifacts and datasets at h p://www.gdprbench.org
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