The Opening Section of Isang Yun's My Land My People: A Cross-Section of Korean and Western Musical Features

Isang Yun's oratorio My Land My People is organized in four movements, and is scored for orchestra, solo voice and choir. Movements are titled as follows: Rjoksa (History), Hyon-Shil I (Presence I), Hyon-shil II (Presence II), and Mi-rae (Future). This document only covers from measures 1-38 of the first section of the first movement of this work. Even though this work is atonal, the composer emphasizes a harmonically moving, tonal sonority: interval class five includes perfect 4th and 5th, quintal-quartal harmony and authentic cadence moving dominant to tonic. Also, in this document, a comparison with Korean traditional music elements is included to support Isang Yun's musical features

Physical Activity Recognition based on Rotated Acceleration Data using Orientation Filter

The purpose of the study was to examine the accuracy of physical activity (PA) classification algorithms using a rotational analysis

Vispark: GPU-accelerated distributed visual computing using spark

With the growing need of big-data processing in diverse application domains, MapReduce (e.g., Hadoop) has become one of the standard computing paradigms for large-scale computing on a cluster system. Despite its popularity, the current MapReduce framework suffers from inflexibility and inefficiency inherent to its programming model and system architecture. In order to address these problems, we propose Vispark, a novel extension of Spark for GPU-accelerated MapReduce processing on array-based scientific computing and image processing tasks. Vispark provides an easy-to-use, Python-like high-level language syntax and a novel data abstraction for MapReduce programming on a GPU cluster system. Vispark introduces a programming abstraction for accessing neighbor data in the mapper function, which greatly simplifies many image processing tasks using MapReduce by reducing memory footprints and bypassing the reduce stage. Vispark provides socket-based halo communication that synchronizes between data partitions transparently from the users, which is necessary for many scientific computing problems in distributed systems. Vispark also provides domain-specific functions and language supports specifically designed for high-performance computing and image processing applications. We demonstrate the performance of our prototype system on several visual computing tasks, such as image processing, volume rendering, K-means clustering, and heat transfer simulation.clos

Vispark: GPU-accelerated distributed visual computing using spark

With the growing need of big data processing in diverse application domains, MapReduce (e.g., Hadoop) becomes one of the standard computing paradigms for large-scale computing on a cluster system. Despite of its popularity, the current MapReduce framework suffers from inflexibility and inefficiency inherent from its programming model and system architecture. In order to address these problems, we propose Vispark, a novel extension of Spark for GPU-accelerated MapReduce processing on array-based scientific computing and image processing tasks. Vispark provides an easy-to-use, Python-like high-level language syntax and a novel data abstraction for MapReduce programming on a GPU cluster system. Vispark introduces a programming abstraction for accessing neighbor data in the mapper function, which greatly simplifies many image processing tasks using MapReduce by reducing memory footprints and bypassing the reduce stage. We demonstrate the performance of our prototype system on several visual computing tasks, such as image processing, and K-means clustering

GPU in-Memory Processing Using Spark for Iterative Computation

Due to its simplicity and scalability, MapReduce has become a de facto standard computing model for big data processing. Since the original MapReduce model was only appropriate for embarrassingly parallel batch processing, many follow-up studies have focused on improving the efficiency and performance of the model. Spark follows one of these recent trends by providing in-memory processing capability to reduce slow disk I/O for iterative computing tasks. However, the acceleration of Spark's in-memory processing using graphics processing units (GPUs) is challenging due to its deep memory hierarchy and host-to-GPU communication overhead. In this paper, we introduce a novel GPU-accelerated MapReduce framework that extends Spark's in-memory processing so that iterative computing is performed only in the GPU memory. Having discovered that the main bottleneck in the current Spark system for GPU computing is data communication on a Java virtual machine, we propose a modification of the current Spark implementation to bypass expensive data management for iterative task offloading to GPUs. We also propose a novel GPU in-memory processing and caching framework that minimizes host-to-GPU communication via lazy evaluation and reuses GPU memory over multiple mapper executions. The proposed system employs message-passing interface (MPI)-based data synchronization for inter-worker communication so that more complicated iterative computing tasks, such as iterative numerical solvers, can be efficiently handled. We demonstrate the performance of our system in terms of several iterative computing tasks in big data processing applications, including machine learning and scientific computing. We achieved up to 50 times speed up over conventional Spark and about 10 times speed up over GPU-accelerated Spark

ViStream : Distributed Streaming Framework using Vivaldi

Advances in data acquisition techniques have resulted in a volume of data that is considerably larger than that accumulated in the past. The influx of data has given rise to a new trend of processing big data through the use of streaming framework. In this paper, we pro- pose a system called ViStream, which executes applications on out-of-core data in Vivaldi. Vivaldi is a domain specific language for heterogeneous distributed systems. We improved the language to enable the rendering of primitives through the use of OpenGL and tested applications that can be processed on out-of-core data. Specifically, three applications were validated. First, we executed an application that uses streaming, that is, histogram matching for 1.3 TB of zebrafish data. Second, we ran combined visualization with volume and primitive rendering. Finally, we executed an application that shows only projected volumetric data on a 3D skeletal representation of zebrafish