Recent years have seen a steady rise in the abilities of wheeled-legged balancing robots. Yet, their use is still severely restricted by the lack of efficient control algorithms for overcoming obstacles such as stairs. We take a considerable step towards closing this gap by presenting a fast trajectory optimizer for generating trajectories over a large class of challenging terrains. By limiting the underlying modeling to the planar, nonlinear rigid-body dynamics and subdividing the terrain into contact-phases, a tractable nonlinear programming problem is obtained. The model explicitly accounts for contact switches and impacts, traction limits, and actuation bounds. By introducing an arc-length-related parametrization, the trajectories are rendered inherently contact constraint-consistent. We apply our method to the specific case of the wheeled bipedal robot Ascento, for which we derive closed-form expressions of the dynamics equations, including the kinematic loops. To track the trajectories, we propose a simple LQR-based controller. The approach is validated in real-world experiments where we show the execution of trajectories for traversing steps, driving up ramps, jumping, standing up, and driving up entire stairways. To the authors' best knowledge, enabling the latter by means of trajectory optimization is a novelty for wheeled-legged robots.

By combining rotary- and fixed-wing flight, hybrid uncrewed aerial vehicles (H-UAVs) can uniquely address missions combining long-range aerial transport and precise ground-relative tasks, such as the placement or retrieval of payloads. However, to leverage their full maneuverability, first, the fundamentally different operating modes of rotary- and fixed-wing vehicles need to be unified and second, the system be controlled precisely despite complex aerodynamic effects. This work presents a general and lightweight, cascaded control formulation for such versatile and accurate operation of H-UAVs. First, a novel guidance law unifies ground- and air-relative position control modes typical for the individual flight regimes. Second, we formulate a jerk-level feedback-linearization to accurately track the guidance outputs despite model errors and disturbances. In extensive real flight tests with a tiltwing H-UAV, we demonstrate the versatile allocation of (hybrid) flight states and the overall accuracy enabled by the control system. Position errors remain below 0.5 m (one quarter of the wingspan) in the full flight envelope, including accelerated maneuvers up to 10 ms2and gusting wind reaching 12 m/s. Finally, the control system demonstrates exploiting hybrid flight for transport-related missions with a precise, in-flight pickup of a payload.

Hybrid unmanned aerial vehicles (H-UAVs) are highly versatile platforms with the ability to transition between rotary- and fixed-wing flight. However, their (aero)dynamics tend to be highly nonlinear which increases the risk of introducing safety-critical modeling errors in a controller. Designing a safe, yet not too cautious controller, requires a credible model which provides accurate dynamics uncertainty quantification. We present a data-efficient, probabilistic semi-parametric dynamics modeling approach that allows for online, filter-based inference. The proposed model leverages prior knowledge using a nominal parametric model, and combines it with residuals in the form of sparse Gaussian processes to account for possibly unmodeled forces and moments. Uncertain nominal and residual parameters are jointly estimated using Bayesian filtering. The resulting model accuracy and the reliability of its predicted uncertainty are analyzed for both a simulated and a real example, where we learn the 6DoF nonlinear dynamics of a tiltwing H-UAV from a few minutes of flight data. Compared to a residual-free nominal model, the proposed semi-parametric approach provides increased model accuracy in relevant parts of the flight envelope and substantially higher credibility overall.

Failure tolerant data encoding and storage is of paramount importance for data centers, supercomputers, data transfers, and many aspects of information technology. Reed-Solomon failure erasure codes and their variants are the basis for many applications in this field. Efficient implementation of these codes is challenging because they require computations in Galois fields, which are not supported by processors natively. Improved variants such as the Cauchy-Reed-Solomon code enable a better mapping of the required calculations to computer instructions. However, this works best when the source code of the application is tuned for fixed encoding parameters which deteriorates the flexibility. We present an approach to overcoming these limitations by just in time compiling optimized code for arbitrary encoding settings. Our open source library is optimized for x86 processors using SSE and AVX extensions and we present prototypes for GPUs and FPGAs as well. For a significant range of encoding parameters, our implementation encodes at the maximum bandwidth the processor can read the data from memory. In more complicated settings with additional redundancy data to compensate the failure of multiple data stores, the algorithm becomes compute limited. The optimized JIT code leverages the full potential of modern processors reaching an instruction throughput of more than three SIMD-instructions per compute cycle, and encodes up to 19 gigabytes of data per second on a Skylake system.

Today, accelerator cards like GPUs are an important constituent of HPC clusters. For certain GPU-intense applications, the trend is shifting toward multi-GPU systems with four or more GPUs per compute node. This can increase the performance per dollar and the performance per watt. The Linpack benchmark is the standard tool for measuring the compute performance of supercomputers. Its standard implementation, HPL, cannot make use of GPUs on its own and other GPU-enabled versions of HPL show reduced efficiency on multi-GPU systems with four or more GPUs per node. In previous efforts we have developed a GPU-accelerated Linpack implementation in particular for AMD GPUs, which we employed on several clusters of a couple of hundred nodes each. Gustafson's law predicts perfect weak scaling with the number of processor cores, but it is not directly applicable to the number of GPUs in a system due to shared resources. In this paper we develop a model for general relations between the number of GPUs and the minimum problem size required to use them efficiently taking into account the limited PCIe bandwidth, memory bandwidth, and CPU resources. Based on this, we present an approach to scale our Linpack implementation to eight GPUs and achieve good GPU utilization in Linpack on future systems. Finally, we examine how energy efficiency, which has improved significantly with dual-and quad-GPU servers, scales to many-GPU systems.

In recent years, high performance computing has benefitted greatly from special accelerator cards such as GPUs. Matrix multiplication performed by the BLAS function DGEMM is one of the prime examples where such accelerators excel. DGEMM is the computational hotspot of many tasks, among them the Linpack benchmark. Current GPUs achieve more than 1 TFLOPS real performance in this task. Being connected via PCI Express, one can easily install multiple GPUs in a single compute node. This enables the construction of multi-TFLOPS systems out of off-the-shelf components. At such high performance, it is often complicated to feed the GPUs with sufficient data to run at full performance. In this paper we first analyze the scalability of our DGEMM implementation for multiple fast GPUs. Then we suggest a new scheme optimized for this situation and we present an implementation.

The last decade has seen power consumption move from an afterthought to the foremost design constraint of new supercomputers. Measuring the power of a supercomputer can be a daunting proposition, and as a result, many published measurements are extrapolated. This paper explores the validity of these extrapolations in the context of inter-node power variability and power variations over time within a run. We characterize power variability across nodes in systems at eight supercomputer centers across the globe. This characterization shows that the current requirement for measurements submitted to the Green500 and others is insufficient, allowing variations of up to 20% due to measurement timing and a further 10--15% due to insufficient sample sizes. This paper proposes new power and energy measurement requirements for supercomputers, some of which have been accepted for use by the Green500 and Top500, to ensure consistent accuracy.

Computers are essential in research and industry, but they are also significant contributors to the worldwide power consumption. The LOEWE-CSC supercomputer addresses this problem by setting new standards in environmental compatibility as well as energy and cooling efficiency for high-performance and general-purpose computing. Designing a pervasively energy efficient compute center requires improvements in multiple fields. The hosting low-loss compute-center operates at a cooling overhead below 8% of the computer power. General purpose graphics processing units provide more compute performance per watt than standard processors. A balanced hardware configuration ensures that most of the compute power is available to the user when he employs optimized applications. Clever algorithms enable the user to fully exploit the computational potential and avoids to waste power when the processors idles, which is often a cause of inefficient programming. The LOEWE-CSC operated at 740MFlops/W during a Linpack benchmark run, by using commodity servers and ranked place 8 in the Green500 list of November 2010. These innovations provide a fundamental step towards cost-effective, environment-friendly exascale computing and IT operation.