To support data intensive cluster computing, it is increasingly important that node virtual memory (VM) systems make effective use of available fast storage devices for swap or temporary file space. Nswap2L is a novel system that transparently manages a heterogeneous set of storage options commonly found in clusters, including node RAM, disk, flash SSD, PCM, or network storage devices. Nswap2L implements a two-level device driver interface. At the top level, it appears to node operating systems (OSs) as a single, fast, random access device that can be added as a swap partition on cluster nodes. It transparently manages the underlying heterogeneous storage devices, including its own implementation of Network RAM, to which swapped out data are stored. It implements data placement, migration, and prefetching policies that choose which underlying physical devices store swapped-out page data. Its policies incorporate information about device capacity, system load, and the strengths of different physical storage media. By moving device-specific knowledge into Nswap2L, VM policies in the OS can be based solely on typical application access patterns and not on characteristics of underlying physical storage media. Nswap2L's policy decisions are abstracted from the OS, freeing the OS from having to implement specialized policies for different combinations of cluster storage-Nswap2L requires no changes to the OS's VM system. Results of our benchmark tests show that data-intensive applications perform up to 6 times faster on Nswap2L-enabled clusters, and show that our two-level device driver design adds minimal I/O latency to the underlying devices that Nswap2L manages. In addition, we found that even though Nswap2L's Network RAM is faster than any other backing store, its prefetching policy that distributes data over multiple devices results in increased I/O parallelism and can lead to better performance than swapping only to a single underlying device.

In this paper we describe two interactive virtual musical instruments that are controlled by sound. These instruments are based on virtual physical models that can be pushed and prodded by making sounds into a microphone. These models provide a mapping between acoustic sounds and computergenerated sounds and visuals. KeywordsMusic, interaction design, physical models.

We present Nswap2L-FS, a fast, adaptable, and heterogeneous storage system for backing file data in clusters. Nswap2L-FS particularly targets backing temporary files, such as those created by data-intensive applications for storing intermediate results. Our work addresses the problem of how to efficiently and effectively make use of heterogeneous storage devices that are increasingly common in clusters. Nswap2L-FS implements a two-layer device design. The top layer transparently manages a set of bottom layer physical storage devices, which may include SSD, HDD, and its own implementation of network RAM. Nswap2L-FS appears to node operating systems as a single, fast backing storage device for file systems, hiding the complexity of heterogeneous storage management from OS subsystems. Internally, it implements adaptable and tunable policies that specify where data should be placed and whether data should be migrated from one underlying physical device to another based on resource usage and the characteristics of different devices. We present solutions to challenges that are specific to supporting backing filesystems, including how to efficiently support a wide range of I/O request sizes and balancing fast storage goals with expectations of persistence of stored file data. Nswap2L-FS defines relaxed persistence guarantees on individual file writes to achieve faster I/O accesses; less stringent persistence semantics allow it to make use of network RAM to store file data, resulting in faster file I/O to applications. Relaxed persistence guarantees are acceptable in many situations, particularly those involving short-lived data such as temporary files. Nswap2L-FS provides a persistence snapshot mechanism that can be used by applications or checkpointing systems to ensure that file data are persistent at certain points in their execution. Nswap2L-FS is implemented as a Linux block device driver that can be added as a file partition on individual cluster nodes. Experimental results show that file-intensive applications run faster when using Nswap2L-FS as backing store. Additionally, its adaptive data placement and migration policies, which make effective use of different underlying physical storage devices, result in performance exceeding that of any single device.

This paper presents our experiences, motivations, and goals for developing Dive into Systems [17], a new, free, online textbook that introduces computer systems, computer organization, and parallel computing. Our book's topic coverage is designed to give readers a gentle and broad introduction to these important topics. It teaches the fundamentals of computer systems and architecture, introduces skills for writing efficient programs, and provides necessary background to prepare students for advanced study in computer systems topics. Our book assumes only a CS1 background of the reader and is designed to be useful to a range of courses-as a primary textbook for courses that introduce computer systems topics or as an auxiliary textbook to provide systems background in other courses. Results of an evaluation from students and faculty at 18 institutions who used a beta release of our book show overwhelmingly strong support for its coverage of computer systems topics, its readability, and its availability. Chapters are reviewed and edited by external volunteers from the CS education community. Their feedback, as well as that of student and faculty users, is continuously incorporated into its online content. We anticipate releasing version 1.0 of the book in spring of 2021, and a release candidate is currently available at https://diveintosystems.org. CCS CONCEPTS• Applied computing → Education; Publishing; • Social and professional topics → Student assessment; • Computing methodologies → Parallel computing methodologies.