Xiaojun Cai

For a long time, computer science has been one of the most popular majors in higher education. To cultivate well-rounded students, ideological and political education is necessary to be incorporated into the process of course construction. In this paper, taking “Computer Organization and Design” as an example, we firstly construct a case library for ideological and political education. Secondly, to organically incorporate these ideological and political cases into the teaching process, we propose a BOPPPS-based teaching framework. Thirdly, we establish a multi-dimensional evaluation system that takes the achievement of ideological and political education into consideration. Practice has proved that the proposed teaching framework achieves a significant improvement in talents cultivation.

Non-volatile processors are proposed for ambient energy harvesting systems to enable accumulative computing across power failures. They employ nonvolatile memory for processor status backup before power outage and resume the system after power recovers. A straightforward backup policy is to back up all volatile data in processors, but it induces high backup cost. In this paper, we focus on branch predictor, an important component in processor, and propose efficient backup schemes to reduce backup cost while maintaining its prediction ability. We first analyze the modules in both traditional and artificial intelligence (AI) assisted designs of branch predictor, and accordingly propose three backup mechanisms pertaining to saturation-driven, locality-driven and maturity-driven backup. On the basis of these mechanisms, adaptive backup branch predictors are designed. Evaluation shows that, with traditional Tournament architecture, the proposed design achieves 15.9% and 54.1% energy reduction when compared with no-backup and all-backup strategy. For AI assisted branch predictor, the proposed design achieves 27.5% and 82.2% energy saving.

The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for persistence. However, blockchain semantics are ignored during this process, making the blockchain storage suffer from heavy read/write amplification problems. Moreover, as the Ethereum network scales up, tremendous data further exacerbates its storage burden. Until now, most studies have focused on sharding, data archiving, decentralized distributed storage, etc., to mitigate the burden of the storage layer. However, the incompatibility between Ethereum semantics and the characteristics of the storage engine is ignored. In this paper, we present ChainKV, a new semantics-aware storage paradigm to improve the storage management performance for the Ethereum system. Firstly, based on Ethereum blockchain semantics, ChainKV separately stores different types of data in multiple storage zones in the KV store to mitigate the read/write amplification problem. Secondly, following the mechanism of the verification process in the authenticated data structure (ADS), a new ADS data transformer is proposed to exploit the data locality when persisting ADS. Moreover, a new space gaming caching policy is adopted to coordinate the cache space management for two independent storage zones. Finally, we propose an optional lightweight node crash recovery mechanism to eliminate functional redundancy between the Ethereum protocol and the storage engine. The experimental results indicate that ChainKV outperforms the prior Ethereum systems by up to 1.99× and 4.20× for synchronization and query operations, respectively