BAG : Managing GPU as buffer cache in operating systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment

Analyzing the impact of storage shortage on data availability in decentralized online social networks

Maintaining data availability is one of the biggest challenges in decentralized online social networks (DOSNs). The existing work often assumes that the friends of a user can always contribute to the sufficient storage capacity to store all data. However, this assumption is not always true in today’s online social networks (OSNs) due to the fact that nowadays the users often use the smart mobile devices to access the OSNs. The limitation of the storage capacity in mobile devices may jeopardize the data availability. Therefore, it is desired to know the relation between the storage capacity contributed by the OSN users and the level of data availability that the OSNs can achieve. This paper addresses this issue. In this paper, the data availability model over storage capacity is established. Further, a novel method is proposed to predict the data availability on the fly. Extensive simulation experiments have been conducted to evaluate the effectiveness of the data availability model and the on-the-fly prediction

Fast Parallel Molecular Algorithms for DNA-Based Computation: Solving the Elliptic Curve Discrete Logarithm Problem over GF(2n)

Elliptic curve cryptographic algorithms convert input data to unrecognizable encryption and the unrecognizable data back again into its original decrypted form. The security of this form of encryption hinges on the enormous difficulty that is required to solve the elliptic curve discrete logarithm problem (ECDLP), especially over GF(2n), n ∈ Z+. This paper describes an effective method to find solutions to the ECDLP by means of a molecular computer. We propose that this research accomplishment would represent a breakthrough for applied biological computation and this paper demonstrates that in principle this is possible. Three DNA-based algorithms: a parallel adder, a parallel multiplier, and a parallel inverse over GF(2n) are described. The biological operation time of all of these algorithms is polynomial with respect to n. Considering this analysis, cryptography using a public key might be less secure. In this respect, a principal contribution of this paper is to provide enhanced evidence of the potential of molecular computing to tackle such ambitious computations