Computer architecture simulators are widely used to explore new architectures, e.g., the gem5 simulator. However, gem5 has significant performance errors that may lead to misleading research results. Researchers typically reduce errors with the target machine by manual calibration methods, which are time-consuming and require significant expertise. This paper presents gem5Tune, a parameter auto-tuning framework for the gem5 simulator to reduce errors. Applying black-box optimization (BBO) methods, recommended for TPE-based Bayesian optimization, gem5Tune minimizes the error between gem5 and the target machine within a limited number of iterations. Three optimization methods, instruction calibration, sensitivity analysis, and dynamic pruning, are proposed to accelerate the error convergence. Experimental results show that compared to the manual calibration method, gem5Tune significantly reduces performance errors between gem5 and three modern ARM servers by more than 10% (13.83%, 10.86%, and 25.22%, respectively) for SPEC CPU benchmarks. It also scales effectively to PARSEC and SPLASH-2x benchmarks and reduces the errors of architectural events.

Bare-metal instances are crucial for high-value, mission-critical applications on the cloud. Tenants exclusively use these dedicated hardware resources. Local virtualized disks are essential for bare-metal instances to provide flexible and high-performance storage resources. Traditionally tenants can choose polling-based software virtualization techniques, but they consume too many valuable host CPU cores and suffer from performance degradation. Cloud vendors are hard to deploy existing hardware-assisted local storage solutions in bare-metal instances due to no access to the host OS to install customized drivers. Moreover, cloud vendors have difficulties managing and maintaining the local storage devices in bare-metal instances because hardware resources and host operating systems are completely utilized by tenants, then it will impact the availability of storage devices.This paper presents our design and experience with BM-Store, a novel high-performance hardware-assisted virtual local storage architecture for bare-metal clouds. BM-Store is transparent to the host that tenants are unaware of the underlying hardware architecture. Therefore, it can be deployed on a large scale in cloud vendors. BM-Store consists of two components: an FPGA-based BMS-Engine and an ARM-based BMS-Controller. The BMS-Engine accelerates the I/O path to enable high-performance virtual storage independent of disk devices without consuming any CPU resource on the host. The BMS-Controller is responsible for resource management and maintenance to achieve flexible and high available local storage. The results of the extensive experiments show that BM-Store can achieve near-native performance, which only introduces about 3 µs extra latency and average 4.0% throughput overhead to native disks. Compared to SPDK vhost, BM-Store achieves an average bandwidth improvement of 15.7% in microbenchmark and a maximum throughput enhancement of 13.4% in real-world applications.

The JPEG is one of the most widely used lossy image-compression standards, whose compression performance depends largely on a quantization table. In this work, we utilize a Convolutional Neural Network (CNN) to generate an image-adaptive quantization table in a standard-compliant way. We first build an image set containing more than 10,000 images and generate their optimal quantization tables through a classical genetic algorithm, and then propose a method that can efficiently extract and fuse the frequency and spatial domain information of each image to train a regression network to directly generate adaptive quantization tables. In addition, we extract several representative quantization tables from the dataset and train a classification network to indicate the optimal one for each image, which further improves compression performance and computational efficiency. Tests on diverse images show that the proposed method clearly outperforms the state-of-the-art method. Compared with the standard table at the compression rate of 1.0 bpp, the regression and classification network provide average Peak Signal-to-Noise Ratio (PSNR) gains of nearly 1.2 and 1.4 dB. For the experiment under Structural Similarity Index Measurement (SSIM), the improvements are 0.4% and 0.54%, respectively. The proposed method also has competitive computational efficiency, as the regression and classification network only take 15 and 6.25 milliseconds, respectively, to process a 768 W 512 image on a single CPU core at 3.20 GHz.