Geographic Information Science

This chapter begins with a definition of geographic information science (GIScience). We then discuss how this research area has been influenced by recent developments in computing and data-intensive analysis, before setting out its core organizing principles from a practical perspective. The following section reflects on the key characteristics of geographic information, the problems posed by large data volumes, the relevance of geographic scale, the remit of geographic simulation, and the key achievements of GIScience to date. Our subsequent review of changing scientific practices and the changing problems facing scientists addresses developments in high-performance computing, heightened awareness of the social context of geographic information systems (GISystems), and the importance of neogeography in providing new data sources, in driving the need for new techniques, and in heightening a human-centric perspective

A Flexible Architecture for the Computation of Direct and Inverse Transforms in H.264/AVC Video Codecs

A new high throughput and scalable architecture for unified transform coding in H.264/AVC is proposed in this paper. Such flexible structure is capable of computing all the 4x4 and 2x2 transforms for Ultra High Definition Video (UHDV) applications (4320x7680@ 30fps) in real-time and with low hardware cost. These significantly high performance levels were proven with the implementation of several different configurations of the proposed structure using both FPGA and ASIC 90 nm technologies. In addition, such experimental evaluation also demonstrated the high area efficiency of theproposed architecture, which in terms of Data Throughput per Unit of Area (DTUA) is at least 1.5 times more efficient than its more prominent related designs(1)

The Weight Function in the Subtree Kernel is Decisive

Tree data are ubiquitous because they model a large variety of situations, e.g., the architecture of plants, the secondary structure of RNA, or the hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data is difficult per se. In this paper, we focus on the subtree kernel that is a convolution kernel for tree data introduced by Vishwanathan and Smola in the early 2000's. More precisely, we investigate the influence of the weight function from a theoretical perspective and in real data applications. We establish on a 2-classes stochastic model that the performance of the subtree kernel is improved when the weight of leaves vanishes, which motivates the definition of a new weight function, learned from the data and not fixed by the user as usually done. To this end, we define a unified framework for computing the subtree kernel from ordered or unordered trees, that is particularly suitable for tuning parameters. We show through eight real data classification problems the great efficiency of our approach, in particular for small datasets, which also states the high importance of the weight function. Finally, a visualization tool of the significant features is derived.Comment: 36 page

HyperFPGA: SoC-FPGA Cluster Architecture for Supercomputing and Scientific applications

Since their inception, supercomputers have addressed problems that far exceed those of a single computing device. Modern supercomputers are made up of tens of thousands of CPUs and GPUs in racks that are interconnected via elaborate and most of the time ad hoc networks. These large facilities provide scientists with unprecedented and ever-growing computing power capable of tackling more complex and larger problems. In recent years, the most powerful supercomputers have already reached megawatt power consumption levels, an important issue that challenges sustainability and shows the impossibility of maintaining this trend. With more pressure on energy efficiency, an alternative to traditional architectures is needed. Reconfigurable hardware, such as FPGAs, has repeatedly been shown to offer substantial advantages over the traditional supercomputing approach with respect to performance and power consumption. In fact, several works that advanced the field of heterogeneous supercomputing using FPGAs are described in this thesis \cite{survey-2002}. Each cluster and its architectural characteristics can be studied from three interconnected domains: network, hardware, and software tools, resulting in intertwined challenges that designers must take into account. The classification and study of the architectures illustrate the trade-offs of the solutions and help identify open problems and research lines, which in turn served as inspiration and background for the HyperFPGA. In this thesis, the HyperFPGA cluster is presented as a way to build scalable SoC-FPGA platforms to explore new architectures for improved performance and energy efficiency in high-performance computing, focusing on flexibility and openness. The HyperFPGA is a modular platform based on a SoM that includes power monitoring tools with high-speed general-purpose interconnects to offer a great level of flexibility and introspection. By exploiting the reconfigurability and programmability offered by the HyperFPGA infrastructure, which combines FPGAs and CPUs, with high-speed general-purpose connectors, novel computing paradigms can be implemented. A custom Linux OS and drivers, along with a custom script for hardware definition, provide a uniform interface from application to platform for a programmable framework that integrates existing tools. The development environment is demonstrated using the N-Queens problem, which is a classic benchmark for evaluating the performance of parallel computing systems. Overall, the results of the HyperFPGA using the N-Queens problem highlight the platform's ability to handle computationally intensive tasks and demonstrate its suitability for its use in supercomputing experiments.Since their inception, supercomputers have addressed problems that far exceed those of a single computing device. Modern supercomputers are made up of tens of thousands of CPUs and GPUs in racks that are interconnected via elaborate and most of the time ad hoc networks. These large facilities provide scientists with unprecedented and ever-growing computing power capable of tackling more complex and larger problems. In recent years, the most powerful supercomputers have already reached megawatt power consumption levels, an important issue that challenges sustainability and shows the impossibility of maintaining this trend. With more pressure on energy efficiency, an alternative to traditional architectures is needed. Reconfigurable hardware, such as FPGAs, has repeatedly been shown to offer substantial advantages over the traditional supercomputing approach with respect to performance and power consumption. In fact, several works that advanced the field of heterogeneous supercomputing using FPGAs are described in this thesis \cite{survey-2002}. Each cluster and its architectural characteristics can be studied from three interconnected domains: network, hardware, and software tools, resulting in intertwined challenges that designers must take into account. The classification and study of the architectures illustrate the trade-offs of the solutions and help identify open problems and research lines, which in turn served as inspiration and background for the HyperFPGA. In this thesis, the HyperFPGA cluster is presented as a way to build scalable SoC-FPGA platforms to explore new architectures for improved performance and energy efficiency in high-performance computing, focusing on flexibility and openness. The HyperFPGA is a modular platform based on a SoM that includes power monitoring tools with high-speed general-purpose interconnects to offer a great level of flexibility and introspection. By exploiting the reconfigurability and programmability offered by the HyperFPGA infrastructure, which combines FPGAs and CPUs, with high-speed general-purpose connectors, novel computing paradigms can be implemented. A custom Linux OS and drivers, along with a custom script for hardware definition, provide a uniform interface from application to platform for a programmable framework that integrates existing tools. The development environment is demonstrated using the N-Queens problem, which is a classic benchmark for evaluating the performance of parallel computing systems. Overall, the results of the HyperFPGA using the N-Queens problem highlight the platform's ability to handle computationally intensive tasks and demonstrate its suitability for its use in supercomputing experiments

MPI Application Binary Interface Standardization

MPI is the most widely used interface for high-performance computing (HPC) workloads. Its success lies in its embrace of libraries and ability to evolve while maintaining backward compatibility for older codes, enabling them to run on new architectures for many years. In this paper, we propose a new level of MPI compatibility: a standard Application Binary Interface (ABI). We review the history of MPI implementation ABIs, identify the constraints from the MPI standard and ISO C, and summarize recent efforts to develop a standard ABI for MPI. We provide the current proposal from the MPI Forum's ABI working group, which has been prototyped both within MPICH and as an independent abstraction layer called Mukautuva. We also list several use cases that would benefit from the definition of an ABI while outlining the remaining constraints

Advanced Simulation Capability for Environmental Management: Development and Demonstrations (12532)

ABSTRACT The U.S. Department of Energy Office of Environmental Management (EM), Technology Innovation and Development is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is a state-of-the-art scientific tool and approach for understanding and predicting contaminant fate and transport in natural and engineered systems. The modular and open source high-performance computing tool facilitates integrated approaches to modeling and site characterization that enable robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project continues to make significant progress in development of capabilities, which are organized into Platform and Integrated Toolsets and a High-Performance Computing Multiprocess Simulator. The Platform capabilities target a level of functionality to allow end-toend model development, starting with definition of the conceptual model and management of data for model input. The High-Performance Computing capabilities target increased functionality of process model representations, toolsets for interaction with Platform, and verification and model confidence testing. The new capabilities are demonstrated through working groups, including one focused on the Hanford Site Deep Vadose Zone

Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way

Existing protocols for benchmarking current quantum co-processors fail to meet the usual standards for assessing the performance of High-Performance-Computing platforms. After a synthetic review of these protocols -- whether at the gate, circuit or application level -- we introduce a new benchmark, dubbed Atos Q-score (TM), that is application-centric, hardware-agnostic and scalable to quantum advantage processor sizes and beyond. The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut combinatorial optimization problem with the Quantum Approximate Optimization Algorithm. We give a robust definition of the notion of effective performance by introducing an improved approximation ratio based on the scaling of random and optimal algorithms. We illustrate the behavior of Q-score using perfect and noisy simulations of quantum processors. Finally, we provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware

Stencil Computation with Vector Outer Product

Matrix computation units have been equipped in current architectures to accelerate AI and high performance computing applications. The matrix multiplication and vector outer product are two basic instruction types. The latter one is lighter since the inputs are vectors. Thus it provides more opportunities to develop flexible algorithms for problems other than dense linear algebra computing and more possibilities to optimize the implementation. Stencil computations represent a common class of nested loops in scientific and engineering applications. This paper proposes a novel stencil algorithm using vector outer products. Unlike previous work, the new algorithm arises from the stencil definition in the scatter mode and is initially expressed with formulas of vector outer products. The implementation incorporates a set of optimizations to improve the memory reference pattern, execution pipeline and data reuse by considering various algorithmic options and the data sharing between input vectors. Evaluation on a simulator shows that our design achieves a substantial speedup compared with vectorized stencil algorithm

Cloud Computing in Resource Management

Swiftly increasing demand of computational calculations in the process of business, transferring of files under certain protocols and data centers force to develop an emerging technology cater to the services for computational need, highly manageable and secure storage. To fulfill these technological desires cloud computing is the best answer by introducing various sorts of service platforms in high computational environment. Cloud computing is the most recent paradigm promising to turn around the vision of “computing utilities” into reality. The term “cloud computing” is relatively new, there is no universal agreement on this definition. In this paper, we go through with different area of expertise of research and novelty in cloud computing domain and its usefulness in the genre of management. Even though the cloud computing provides many distinguished features, it still has certain sorts of short comings amidst with comparatively high cost for both private and public clouds. It is the way of congregating amasses of information and resources stored in personal computers and other gadgets and further putting them on the public cloud for serving users. Resource management in a cloud environment is a hard problem, due to the scale of modern data centers, their interdependencies along with the range of objectives of the different actors in a cloud ecosystem. Cloud computing is turning to be one of the most explosively expanding technologies in the computing industry in this era. It authorizes the users to transfer their data and computation to remote location with minimal impact on system performance. With the evolution of virtualization technology, cloud computing has been emerged to be distributed systematically or strategically on full basis. The idea of cloud computing has not only restored the field of distributed systems but also fundamentally changed how business utilizes computing today. Resource management in cloud computing is in fact a typical problem which is due to the scale of modern data centers, the variety of resource types and their inter dependencies, unpredictability of load along with the range of objectives of the different actors in a cloud ecosystem