Abstract-Nowadays Graphic Processing Units (GPU) are gaining increasing popularity in high performance computing (HPC). While modern GPUs can offer much more computational power than CPUs, they also consume much more power. Energy efficiency is one of the most important factors that will affect a broader adoption of GPUs in HPC. In this paper, we systematically characterize the power and energy efficiency of GPU computing. Specifically, using three different applications with various degrees of compute and memory intensiveness, we investigate the correlation between power consumption and different computational patterns under various voltage and frequency levels. Our study revealed that energy saving mechanisms on GPUs behave considerably different than CPUs. The characterization results also suggest possible ways to improve the "greenness" of GPU computing.

Hybrid memory designs, such as DRAM plus Phase Change Memory (PCM), have shown some promise for alleviating power and density issues faced by traditional memory systems. But previous studies have concentrated on CPU systems with a modest level of parallelism. This work studies the problem in a massively parallel setting. Specifically, it investigates the special implications to hybrid memory imposed by the massive parallelism in GPU. It empirically shows that, contrary to promising results demonstrated for CPU, previous designs of PCM-based hybrid memory result in significant degradation to the energy efficiency of GPU. It reveals that the fundamental reason comes from a multi-facet mismatch between those designs and the massive parallelism in GPU. It presents a solution that centers around a close cooperation between compiler-directed data placement and hardware-assisted runtime adaptation. The co-design approach helps tap into the full potential of hybrid memory for GPU without requiring dramatic hardware changes over previous designs, yielding 6% and 49% energy saving on average compared to pure DRAM and pure PCM respectively, and keeping performance loss less than 2%.

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The B ε-tree File System, or BetrFS (pronounced "better eff ess"), is the first in-kernel file system to use a write-optimized data structure (WODS). WODS are promising building blocks for storage systems because they support both microwrites and large scans efficiently. Previous WODS-based file systems have shown promise but have been hampered in several ways, which BetrFS mitigates or eliminates altogether. For example, previous WODS-based file systems were implemented in user space using FUSE, which superimposes many reads on a write-intensive workload, reducing the effectiveness of the WODS. This article also contributes several techniques for exploiting write-optimization within existing kernel infrastructure. BetrFS dramatically improves performance of certain types of large scans, such as recursive directory traversals, as well as performance of arbitrary microdata operations, such as file creates, metadata updates, and small writes to files. BetrFS can make small, random updates within a large file 2 orders of magnitude faster than other local file systems. BetrFS is an ongoing prototype effort and requires additional data-structure tuning to match current general-purpose file systems on some operations, including deletes, directory renames, and large sequential writes. Nonetheless, many applications realize significant performance improvements Vrushali Kulkarni, Oleksii Starov, Sourabh Yerfule, and Ahmad Zaraei contributed to the BetrFS prototype.