An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Mobile cloud computing

As mobile network infrastructures continuously improve, they are becoming popular clients to consume any Web resources, especially Web Services (WS). However, there are problems in connecting mobile devices to existing WS. This thesis focuses on three of the following challenge: loss of connection, bandwidth/latency, and limited resources. This research implements and develops a cross-platform architecture for connecting mobile devices to the WS. The architecture includes a platform independent design of mobile service client and a middleware for enhancing the interaction between mobile clients and WS. The middleware also provides a personal service mashup platform for the mobile client. Finally, the middleware can be deployed on Cloud Platforms, like Google App Engine and Amazon EC2, to enhance the scalability and reliability. The experiments evaluate the optimization/adaptation, overhead of the middleware, middleware pushing via email, and performance of Cloud Platforms

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

AUTOMATING DATA-LAYOUT DECISIONS IN DOMAIN-SPECIFIC LANGUAGES

A long-standing challenge in High-Performance Computing (HPC) is the simultaneous achievement of programmer productivity and hardware computational efficiency. The challenge has been exacerbated by the onset of multi- and many-core CPUs and accelerators. Only a few expert programmers have been able to hand-code domain-specific data transformations and vectorization schemes needed to extract the best possible performance on such architectures. In this research, we examined the possibility of automating these methods by developing a Domain-Specific Language (DSL) framework. Our DSL approach extends C++14 by embedding into it a high-level data-parallel array language, and by using a domain-specific compiler to compile to hybrid-parallel code. We also implemented an array index-space transformation algebra within this high-level array language to manipulate array data-layouts and data-distributions. The compiler introduces a novel method for SIMD auto-vectorization based on array data-layouts. Our new auto-vectorization technique is shown to outperform the default auto-vectorization strategy by up to 40% for stencil computations. The compiler also automates distributed data movement with overlapping of local compute with remote data movement using polyhedral integer set analysis. Along with these main innovations, we developed a new technique using C++ template metaprogramming for developing embedded DSLs using C++. We also proposed a domain-specific compiler intermediate representation that simplifies data flow analysis of abstract DSL constructs. We evaluated our framework by constructing a DSL for the HPC grand-challenge domain of lattice quantum chromodynamics. Our DSL yielded performance gains of up to twice the flop rate over existing production C code for selected kernels. This gain in performance was obtained while using less than one-tenth the lines of code. The performance of this DSL was also competitive with the best hand-optimized and hand-vectorized code, and is an order of magnitude better than existing production DSLs.Doctor of Philosoph