ICPP

Wireless Sensor Networks are usually relying on the energy source to the battery, so lifetime of the sensor nodes is limited. Duty Cycling is known as one of the methods to reduce the energy consumption of wireless sensor networks. Therefore, efficient ...

In this paper, we introduce a novel care planning infrastructure platform for reducing the burden on caretakers in nursing homes. The goal of this study is to empirically confirm that the burden on caretakers working at a real nursing home is ...

The effect of growing population brings heavy traffic which in turn leads to increased number of traffic accidents. In particular, the majority of traffic accidents happen at special intersections in situations such as heavy traffic, poor intersection ...

Increases in peak electricity demands and the growing use of renewable energy --- with associated intermittency and variable output --- present new challenges to electricity service providers (ESPs). ESPs employ demand charges, variable tariffs and ...

In 2017/2018, the Energy Efficient HPC Working Group (EE HPC WG) Dashboard Team conducted an analysis that assessed the current use of information dashboards for operational facility management in major supercomputing centers around the globe, resulting ...

At RIKEN, we are developing supercomputer Fugaku (formerly called as Post-K), which is the successor of K Computer, and aiming for official operation around 2021. It is expected to consume more power than K Computer, and because of the power saving ...

This paper presents design considerations that drive the development of an energy-efficient, high performance computing (HPC) data center facility. Key electrical and mechanical design considerations will be presented that maximize facility efficiency, ...

Energy efficiency and datacentre automation are critical targets of the research and deployment agenda of CINECA and its research partners in the Energy Efficient System Laboratory of the University of Bologna and the Integrated System Laboratory in ETH ...

Sudden and short-duration power swings in modern supercomputers can have a challenging impact on the voltage of the adjacent power grids. The coming age of exascale supercomputers is expected to bring platforms that are capable of power fluctuations of ...

As high-performance computing (HPC) resources continue to grow in size and complexity, so too does the volume and velocity of the operational data that is associated with them. At such scales, new mechanisms and technologies are required to continuously ...

Embedded systems show a tendency of migrating to multicore processors. However, to fully use the potential of multicore processors, it is necessary to partition software into threads that execute concurrently and communicate using shared memory. In ...

Binary translation translates binary programs from one instruction set to another. It is widely used in virtual machines and emulators. We extend mc2llvm, which is an LLVM-based retargetable 32-bit binary translator developed in our lab in the past ...

Recently, Rust has become a popular system programming language and been widely used in microkernel OS designs, cryptocurrency designs, deep learning applications, and web browsers. Rust is designed for highly safe and concurrent systems and provides ...

Machine learning nowadays is profoundly impacting every aspect of our lives. With the evolution of the machine learning, many techniques, such as deep learning, improve the accuracy and performance of machine learning. Deep learning is a set of ML ...

Due to the non-deterministic order of interactions between concurrent threads, testing of concurrent software is a challenge. In order to cope with this challenge, researchers have proposed analysis approaches in which the dynamic-based algorithms (e.g.,...

Performance prediction has always been important in the domain of parallel computing. For programs which are executed on workstation clusters and super computing systems, precise prediction of execution time can help task scheduling and resource ...

Parallel programming methodologies are fundamentally dissimilar to those of conventional programming, and software developers without the requisite skillset often find it difficult to adapt to these new methods. This is particularly true for parallel ...

The advent of multi-/many-core processors in clusters advocates hybrid parallel programming, which combines Message Passing Interface (MPI) for inter-node parallelism with a shared memory model for on-node parallelism. Compared to the traditional hybrid ...

This paper proposes Pyne, a parallel programming framework for developing parallel simulations. Pyne is designed such that parallel applications are developed with sequential programming. Pyne provides a programming environment in which programmers ...

Machine Learning applications now span across multiple domains due to the increase in computational power of modern systems. There has been a recent surge in Machine Learning applications in High Performance Computing (HPC) in an attempt to speed up ...

Faster training of deep neural networks is desired to speed up the research and development cycle in deep learning. Distributed deep learning and second-order optimization methods are two different techniques to accelerate the training of deep neural ...

Current large-scale training of deep neural networks typically employs synchronous stochastic gradient descent that incurs large communication overhead. Instead of optimizing reduction routines as done in recent studies, we propose algorithms that do ...

Today's HPC systems experience steadily increasing problems with the storage I/O bottleneck. At the same time, new storage technologies are emerging in the compute nodes of HPC systems. There are many ideas and approaches how compute-node local storage ...

Nowadays, cloud platforms usually offer several types of Virtual Machines (VMs) which have different guarantees in terms of availability and volatility, provisioning the same resource through multiple pricing models. For instance, in the Amazon EC2 ...

This paper presents a list-based scheduling algorithm called Predict Priority Task Scheduling (PPTS) for heterogeneous computing. The main goal is to minimize the scheduling length by introducing a lookahead feature in the two phases of the PPTS ...

We are in the era of big data and cloud computing, large quantity of computing resource is desperately needed to detect invaluable information hidden in the coarse big data through query evaluation. Users demand big data analytic services with various ...

Topology-aware process mapping can reduce communication cost by embedding the application communication topology to the underlying networks. Being generally a NP-hard problem, process mapping methods strive to balance mapping cost and mapping ...