Investigations into the microwave surface impedance of superconducting resonators have led to the development of single photon counters that rely on kinetic inductance for their operation. While concurrent progress in additive manufacturing, '3D printing', opens up a previously inaccessible design space for waveguide resonators. In this manuscript, we present results from the first synthesis of these two technologies in a titanium, aluminum, vanadium (Ti-6Al-4V) superconducting radio frequency resonator which exploits a design unattainable through conventional fabrication means. We find that Ti-6Al-4V has two distinct superconducting transition temperatures observable in heat capacity measurements. The higher transition temperature is in agreement with DC resistance measurements. While the lower transition temperature, not previously known in literature, is consistent with the observed temperature dependence of the superconducting microwave surface impedance. From the surface reactance, we extract a London penetration depth of 8 ± 3µm -roughly an order of magnitude larger than other titanium alloys and several orders of magnitude larger than other conventional elemental superconductors. This large London penetration depth suggests that Ti-6Al-4V may be a suitable material for high kinetic inductance applications such as single photon counting or parametric amplification used in quantum computing.

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Heterogeneous systems consisting of multi-core CPUs, Graphics Processing Units (GPUs) and many-core accelerators have gained widespread use by application developers and data-center platform developers. Modern day heterogeneous systems have evolved to include advanced hardware and software features to support a spectrum of application patterns. Heterogeneous programming frameworks such as CUDA, OpenCL, and OpenACC have all introduced new interfaces to enable developers to utilize new features on these platforms. In emerging applications, performance optimization is not only limited to effectively exploiting data-level parallelism, but includes leveraging new degrees of concurrency and parallelism to accelerate the entire application.To aid hardware architects and application developers in effectively tuning performance on GPUs, we have developed the NUPAR benchmark suite. The NUPAR applications belong to a number of different scientific and commercial computing domains. These benchmarks exhibit a range of GPU computing characteristics that consider memory-bandwidth limitations, device occupancy and resource utilization, synchronization latency and device-specific compute optimizations. The NUPAR applications are specifically designed to stress new hardware and software features that include: nested parallelism, concurrent kernel execution, shared hostdevice memory and new instructions for precise computation and data movement. In this paper, we focus our discussion on applications developed in CUDA and OpenCL, and focus on high-end server class GPUs. We describe these benchmarks and evaluate their interaction with different architectural features on a GPU. Our evaluation examines the behavior of the advanced hardware features on recently-released GPU architectures.

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