Arm usage has substantially grown in the High-Performance Computing (HPC) community. Japanese supercomputer Fugaku, powered by Arm-based A64FX processors, held the top position on the Top500 list between June 2020 and June 2022, currently sitting in the fourth position. The recently released 7th generation of Amazon EC2 instances for compute-intensive workloads (C7 g) is also powered by Arm Graviton3 processors. Projects like European Mont-Blanc and U.S. DOE/NNSA Astra are further examples of Arm irruption in HPC. In parallel, over the last decade, the rapid improvement of genomic sequencing technologies and the exponential growth of sequencing data has placed a significant bottleneck on the computational side. While most genomics applications have been thoroughly tested and optimized for x86 systems, just a few are prepared to perform efficiently on Arm machines. Moreover, these applications do not exploit the newly introduced Scalable Vector Extensions (SVE). This paper presents GenArchBench, the first genome analysis benchmark suite targeting Arm architectures. We have selected computationally demanding kernels from the most widely used tools in genome data analysis and ported them to Arm-based A64FX and Graviton3 processors. Overall, the GenArch benchmark suite comprises 13 multi-core kernels from critical stages of widely-used genome analysis pipelines, including base-calling, read mapping, variant calling, and genome assembly. Our benchmark suite includes different input data sets per kernel (small and large), each with a corresponding regression test to verify the correctness of each execution automatically. Moreover, the porting features the usage of the novel Arm SVE instructions, algorithmic and code optimizations, and the exploitation of Arm-optimized libraries. We present the optimizations implemented in each kernel and a detailed performance evaluation and comparison of their performance on four different HPC machines (i.e., A64FX, Graviton3, Intel Xeon Skylake Platinum, and AMD EPYC Rome). Overall, the experimental evaluation shows that Graviton3 outperforms other machines on average. Moreover, we observed that the performance of the A64FX is significantly constrained by its small memory hierarchy and latencies. Additionally, as proof of concept, we study the performance of a production-ready tool that exploits two of the ported and optimized genomic kernels.

We present a lightweight technique with which creators can prototype force-sensitive objects by attaching a pair of piezoelectric elements: one a vibration speaker and one a contact microphone. The key idea behind our technique is that touch force, in addition to the way the object is touched, can also be observed as different resonant frequency spectra. We also show that recognition of a touch and estimation of the touch force can be implemented by using the combination of support vector classification (SVC) and support vector regression (SVR). An experiment with an additional pressure sensor revealed that our technique might perform well in estimating touch force. We also show a tool for machine learning based on our technique that uses an animated guide, allowing creators to give the system both the training data and the labels for training machine learning needed for dealing with continuous-valued output such as SVR.

In this paper, we present a novel acoustic touch sensing technique called Touch & Activate. It recognizes a rich context of touches including grasp on existing objects by attaching only a vibration speaker and a piezo-electric microphone paired as a sensor. It provides easy hardware configuration for prototyping interactive objects that have touch input capability. We conducted a controlled experiment to measure the accuracy and trade-off between the accuracy and number of training rounds for our technique. From its results, per-user recognition accuracies with five touch gestures for a plastic toy as a simple example and six hand postures for the posture recognition as a complex example were 99.6% and 86.3%, respectively. Walk up user recognition accuracies for the two applications were 97.8% and 71.2%, respectively. Since the results of our experiment showed a promising accuracy for the recognition of touch gestures and hand postures, Touch & Activate should be feasible for prototype interactive objects that have touch input capability.

The spaceborne synthetic aperture radar (SAR) to be launched on the Japanese Earth Resources Satellite-1 is described. The SAR is operated in the L-band. The mission is mainly dedicated to geological applications. The ground resolution of the processed image is designed as 18 m and the off-nadir angle required to meet geological applications is 35 degrees . The design and performance of the key system parameters are discussed, along with the reasons for choosing such design parameters. The antenna is a thin-flat-foldable configuration that has a 11.9-m by 2.2-m aperture when extended in orbit. The transmitter, receiver, and signal processor are all of the solid-state type to achieve high reliability of operation. They transmit an 1100-W peak (minimum) chirp pulse and receive the return echos and process the echo signals into in-phase and quadrature data streams.>