While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous

In order to efficiently use the future generations of supercomputers, fault tolerance and power consumption are two of the prime challenges anticipated by the High Performance Computing (HPC) community. Checkpoint/Restart (CR) has been and still is the most widely used technique to deal with hard failures. Application-level CR is the most effective CR technique in terms of overhead efficiency but it takes a lot of implementation effort. This work presents the implementation of our C++ based library CRAFT (Checkpoint-Restart and Automatic Fault Tolerance), which serves two purposes. First, it provides an extendable library that significantly eases the implementation of application-level checkpointing. The most basic and frequently used checkpoint data types are already part of CRAFT and can be directly used out of the box. The library can be easily extended to add more data types. As means of overhead reduction, the library offers a build-in asynchronous checkpointing mechanism and also supports the Scalable Checkpoint/Restart (SCR) library for node level checkpointing. Second, CRAFT provides an easier interface for User-Level Failure Mitigation (ULFM) based dynamic process recovery, which significantly reduces the complexity and effort of failure detection and communication recovery mechanism. By utilizing both functionalities together, applications can write application-level checkpoints and recover dynamically from process failures with very limited programming effort. This work presents the design and use of our library in detail. The associated overheads are thoroughly analyzed using several benchmarks.Thomas Zeiser holds a PhD in Computational Fluid Mechanics from the University of Erlangen-Nuremberg. He is now a senior research scientist in the HPC group of RRZE and is among many other things still interested in lattice Boltzmann methods.Georg Hager holds a PhD in Computational Physics from the University of Greifswald. He has been working with high performance systems since 1995, and is now a senior research scientist in the HPC group at RRZE. Recent research includes architecture-specific optimization for current microprocessors, performance modeling on processor and system levels, and the efficient use of hybrid parallel systems. His daily work encompasses all aspects of user support in HPC such as lectures, tutorials, training, code parallelization, profiling and optimization, and the assessment of novel computer architectures and tools.Gerhard Wellein holds a PhD in Solid State Physics from the University of Bayreuth and is a regular Professor at the Department for Computer Science at University of Erlangen. He heads the HPC group at RRZE and has more than 10 years of experience in teaching HPC techniques to students and scientists from Computational Science and Engineering. His research interests include solving large sparse eigenvalue problems, novel parallelization approaches, performance modeling, and architecture-specific optimization.

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Drainage responds rapidly to tectonic changes and thus it is a potential parameter for tectonogeomorphological analysis. Drainage network of Potwar is a good geological record of movement, displacements, regional uplifts and erosion of the tectonic units. This study focuses on utilizing drainage network extracted from Shuttle Radar Digital Elevation Data (SRTM-DEM) in order to constrain the structure of the Potwar Plateau. SWAN syncline divides Potwar into northern Potwar deformed zone (NPDZ) and southern Potwar platform zone (SPPZ). We extracted the drainage network from DEM and analyzed 112 streams using stream power law. Spatial distribution of concavity and steepness indices were used to prepare uplift rate map for the area. DEM was further utilized to extract lineaments to study the mutual relationship between lineaments and drainage patterns. We compared the local correlation between the extracted lineaments and drainage network of the area that gives us quantitative information and shows promising prospects. The streams in the NPDZ indicate high steepness values as compared to the streams in the SPPZ. The spatial distribution of geomorphic parameters and uplift rates suggest the distinctive deformation among eastern, central and western parts. The local correlation between drainage network and lineaments from DEM is strongly positive in the area within 1 km of radius.

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