第127回計算科学コロキウムを、8月19日（月）に開催いたします。多数のご来聴をお待ちしております。

場所　　　　：筑波大学　計算科学研究センター　ワークショップ室
日時　　　　：2019年8月19日（月）14:00 – 15:00

講演タイトル：VERSE: Versatile Graph Embeddings from Similarity Measures (WWW’18)

講演者氏名　：Panagiotis Karras 准教授（デンマーク オーフス大）   

使用言語　　：英語

Abstract:
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good result as the non-scalable  full variant.

世話人：天笠俊之

第125回計算科学コロキウムを、4月10日（水）に開催いたします。
多数のご来聴をお願いいたします。

場所：筑波大学　計算科学研究センター　ワークショップ室
日時：2019年4月10日（水）13:00 – 14:00
講演タイトル：In situ data processing computational scientific data using the ADIOS framework

講演者氏名　： Dr. Scott A. Klasky, Oak Ridge National Laboratory

使用言語　　：英語

Abstract:
The USA Exascale Computing Project (ECP) is focused on accelerating the delivery of a capable exascale computing ecosystem that delivers 50 times more computational science and data analytic application power than possible with DOE HPC systems such as Titan (ORNL) and Sequoia (LLNL). As next generation applications and experiments grow in concurrency and in complexity, the data produced often grows to extreme levels, limiting scientific knowledge discovery.
In my presentation, I will talk about the new set of applications and experiments which push the edge of scientific data processing and simulation. I will present some of the exciting new research in this area to cope with this tsunami of data, along with the challenges in implementing these effectively on next-generation computer architectures. In my presentation I will also focus on the ADIOS framework (https://www.olcf.ornl.gov/center-projects/adios/) a next generation to ingest, reduce, and move data on HPC systems and over the WAN to other computational resources. I will also focus on in situ data processing infrastructure and next generation data compression algorithms.

Bio:
Scott A. Klasky is a distinguished scientist and the group leader for Scientific Data in the Computer Science and Mathematics Division at the Oak Ridge National Laboratory. He holds an appointment at the University of Tennessee, and Georgia Institute of Technology. He obtained his Ph.D. in Physics from the University of Texas at Austin (1994), specializing in general relativity. Dr. Klasky is a world expert in scientific computing and scientific data management, co- authoring over 200 papers.

世話人：朴 泰祐