Aiming to ease the parallel programming for heterogeneous architectures, we propose and implement a high-level OpenCL runtime that conceptually merges multiple heterogeneous hardware devices into one virtual heterogeneous compute device (VHCD). Moreover, automated workload distribution among the devices is based on offline profiling, together with new programming directives that define the device-independent data access range per work-group. Therefore, an OpenCL program originally written for a single compute device can, after inserting a small number of programming directives, run efficiently on a platform consisting of heterogeneous compute devices. Performance is ensured by introducing the technique of virtual cache management, which minimizes the amount of host-device data transfer. Our new OpenCL runtime is evaluated by a diverse set of OpenCL benchmarks, demonstrating good performance on various configurations of a heterogeneous system.

We review our recent work on ultra-long-haul wavelength division multiplexed (WDM) transmission with high spectral efficiency (SE) employing tight pre-filtering and multi-symbol detection. We start the discussion with a theoretical evaluation of the SE limit of pre-filtered modulation in optical fiber communication systems. We show that pre-filtering induced symbol correlation generates a modulation with memory and thus, a higher SE limit than that of the original memory-less modulation. We also investigate the merits of utilizing the pre-filtering induced symbol correlation with multi-symbol detection to achieve high SE transmission. We demonstrate transoceanic WDM transmission of a pre-filtered polarization division multiplexed return-to-zero quaternary phased shift keying (PDM-RZ-QPSK) modulation format with multi-symbol detection, achieving 419% SE which is higher than the SE limit of the original memory-less PDM-RZ-QPSK format.

This paper presents a study of the applicability of clusters of GPUs to high-resolution 3D simulations of cardiac electrophysiology. By experimenting with representative cardiac cell models and ODE solvers, in association with solving the monodomain equation, we quantitatively analyze the obtainable computational capacity of GPU clusters. It is found that for a 501×501×101 3D mesh, which entails a 0.1mm spatial resolution, a 128-GPU cluster only needs a few minutes to carry out a 100,000-time-step cardiac excitation simulation that involves a four-variable cell model. Even higher spatial and temporal resolutions are achievable for such simplified mathematical models. On the other hand, our experiments also show that a dramatically larger cluster of GPUs is needed to handle a very detailed cardiac cell model.