Javier Garcia-Blas

Optimizations to enhance sustainability of MPI applications

Ultrascale computing systems are likely to reach speeds of two or three orders of magnitude greater than todays computing systems. However, to achieve this level of performance, we need to design and implement more sustainable solutions for ultra-scale computing systems, at both the hardware and software levels, while understanding sustainability in a holistic manner in order to address challenges in economy-of-scale, agile elastic scalability, heterogeneity, programmability, fault resilience, energy efficiency, and storage. Some solutions could be integrated into MPI, but others should be devised as higher level concepts, less general, but adapted to applicative domains, possibly as programming patterns or libraries. In this paper, we layout some proposals to extend MPI to cover major relevant domains in a move towards sustainability, including: MPI programming optimizations and programming models, resilience, data management, and their usage for applications.

FUX-Sim: Implementation of a fast universal simulation/reconstruction framework for X-ray systems

The availability of digital X-ray detectors, together with advances in reconstruction algorithms, creates an opportunity for bringing 3D capabilities to conventional radiology systems. The downside is that reconstruction algorithms for non-standard acquisition protocols are generally based on iterative approaches that involve a high computational burden. The development of new flexible X-ray systems could benefit from computer simulations, which may enable performance to be checked before expensive real systems are implemented. The development of simulation/reconstruction algorithms in this context poses three main difficulties. First, the algorithms deal with large data volumes and are computationally expensive, thus leading to the need for hardware and software optimizations. Second, these optimizations are limited by the high flexibility required to explore new scanning geometries, including fully configurable positioning of source and detector elements. And third, the evolution of the various hardware setups increases the effort required for maintaining and adapting the implementations to current and future programming models. Previous works lack support for completely flexible geometries and/or compatibility with multiple programming models and platforms. In this paper, we present FUX-Sim, a novel X-ray simulation/reconstruction framework that was designed to be flexible and fast. Optimized implementation for different families of GPUs (CUDA and OpenCL) and multi-core CPUs was achieved thanks to a modularized approach based on a layered architecture and parallel implementation of the algorithms for both architectures. A detailed performance evaluation demonstrates that for different system configurations and hardware platforms, FUX-Sim maximizes performance with the CUDA programming model (5 times faster than other state-of-the-art implementations). Furthermore, the CPU and OpenCL programming models allow FUX-Sim to be executed over a wide range of hardware platforms.

Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in cloud environments

In the current scientific computing scenario storage systems are one of the main bottlenecks in computing platforms. This issue affects both traditional high performance computing systems and modern systems based on cloud platforms. Accelerating the I/O subsystems can improve the overall performance of the applications. In this paper, we present Hercules as an I/O accelerator specially designed for improving I/O access in workflow engines deployed over cloud-based infraestructures. Hercules provides a dynamic and flexible in-memory storage platform based on NoSQL-based distributed memory systems. In addition, Hercules offers a user-level interface based on POSIX for facilitating its usage on existing solutions and legacy applications. We have evaluated the proposed solution in a public cloud environment, in this case Amazon EC2. The results show that Hercules provides a scalable I/O solution with remarkable performance, especially for write operations, compared with classic I/O approaches for high performance computing in cloud environments.

New directions in mobile, hybrid, and heterogeneous clouds for cyberinfrastructures

Abstract With the increasing availability of mobile devices and data generated by end-users, scientific instruments and simulations solving many of our most important scientific and engineering problems require innovative technical solutions. These solutions should provide the whole chain to process data and services from the mobile users to the cloud infrastructure, which must also integrate heterogeneous clouds to provide availability, scalability, and data privacy. This special issue presents the results of particular research works showing advances on mobile, hybrid, and heterogeneous clouds for modern cyberinfrastructures.

GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems

BackgroundStandard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption.ResultsWe present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>10243 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden.ConclusionEfficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

Virtual Environments and Advanced Interfaces

Editorial for the Personal Ubiquitous Computing: Special Issue on Virtual Environments and Advanced Interfaces

Algorithms and applications towards the convergence of high-end data-intensive and computing systems

With the increasing availability of data generated by scientific instruments and simulations, today, solving many of our most important scientific and engineering problems requires high-end computing systems (HECS)1 that may be able to process and storage a huge amount of data.2 With this landscape, many synergies between extreme-scale computing, simulations, and data intensive applications might arise.3,4 However, the high-performance computing and data analysis platforms, paradigms, and tools have evolved in many cases in different fields, having their own specific methodologies, tools, and techniques. We need to evolve systems and paradigms to create High-End Data-Intensive Computing Systems (HEDICS) to create high-end resources that must be powerful enough in a broad sense (computation, storage, I/O capacity, communications, etc), but at the same time have to provide utilities from the Big Data computing (BDC) space to satisfy the data management and analytics needs of near future applications. Future HECS platforms will be likely characterized by a three to four orders of magnitude, increasing in concurrency, a substantially larger storage capacity, and a deepening of the storage hierarchy. Moreover, the advent of the Big Data challenges5 has generated new initiatives closely related to ultrascale computing systems in large scale distributed systems. The current uncoordinated development model of independently applying optimizations at each layer of the system software I/O software stack will not scale to the required levels of distribution, concurrency, storage hierarchy, and capacity.6 Thus, we need reusable, modular, and scalable frameworks for designing high-end reconfigurable computers, including novel data processing building block and innovative programming models. In those aspects, many new topics are open to research: parallel and distributed algorithms for HEDICS; algorithms for aggressive management of information and knowledge from massive data sources; resource management and scheduling in high-end data and computing systems; tools and environments for parallel/distributed high-end software development; new programming models, as well as machine and application abstractions; resilience issues in HEDICS; adaptive software; architectures, networks, and systems suited for extreme-scale and Big Data; massive distributed and parallel data analytics and feature extraction; new I/O and storage systems valid for HEDICS; and novel and redesigned high-end scientific and engineering computing. This special issue is intended to provide an overview of some key topics and state-of-the-art of recent advances in subjects relevant to High-End Data-Intensive Computing Systems. The general objectives are to address, explore, and exchange information on the challenges and current state-of-the-art in HEDICS, new programming models, run-times, and data facilities design and performance, and their application in various science and engineering domains.

Introduction to sustainable ultrascale computing systems and applications

Ultrascale systems are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s systems. Targeting sustainable solutions for ultrascale computing will need cross fertilization among HPC, large-scale distributed systems, and big data management, and the reformulation of many algorithms and applications used today in different areas of research. Such a reformulation has to address different challenges that arise from the different application areas, algorithms, and programs. Challenges include the scalability of the applications programs to use a large number of system resources efficiently, the usage of resilience methods to include mechanisms to enable application programs to react to system failures, and the inclusion of energyawareness features into the applicationprograms to be able to obtain an energy-efficient execution. As a large number of resources has to be controlled when using ultrascale systems, the availability of suitable programming models and environments also plays an important role for ultrascale applications. The programming models for ultrascale computing should provide enough abstractions, such that the application programmer does not need to deal with all low-level details of an efficient execution of the (parallel)