Multi-core systems equipped with micro processing units and accelerators such as digital signal processors (DSPs) and graphics processing units (GPUs) have become a major trend in processor design in recent years in a...
详细信息
Multi-core systems equipped with micro processing units and accelerators such as digital signal processors (DSPs) and graphics processing units (GPUs) have become a major trend in processor design in recent years in attempts to meet ever-increasing application performance requirements. Open Computing Language (OpenCL) is one of the programming languages that include new extensions proposed to exploit the computing power of these kinds of processors. Among the newly extended language features, the single-instruction multiple-data (SIMD) linguistics and vector types are added to OpenCL to exploit hardware features of the accelerators. The addition makes it necessary to consider how traditional compiler data flow analysis can be adopted to meet the optimization requirements of vector linguistics. In this paper, we propose a calculus framework to support the data flow analysis of vector constructs for OpenCL programs that compilers can use to perform SIMD optimizations. We model OpenCL vector operations as data access functions in the style of mathematical functions. We then show that the data flow analysis for OpenCL vector linguistics can be performed based on the data access functions. Based on the information gathered from data flow analysis, we illustrate a set of SIMD optimizations on OpenCL programs. The experimental results incorporating our calculus and our proposed compiler optimizations show that the proposed SIMD optimizations can provide average performance improvements of 22% on x86 CPUs and 4% on advanced micro devices GPUs. For the selected 15 benchmarks, 11 of them are improved on x86 CPUs, and six of them are improved on advanced micro devices GPUs. The proposed framework has the potential to be used to construct other SIMD optimizations on OpenCL programs. Copyright (c) 2015 John Wiley & Sons, Ltd.
Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus,...
详细信息
ISBN:
(纸本)9781479940929
Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus, enterprise tenants demand secure data retrieval and computation solution from the cloud provider, which will allow them to utilize cloud services without the risks of leaking private data to outsiders and even service providers. In this paper, we propose an exclusive-or (XOR) homomorphism encryption scheme to support secure keyword searching on encrypted data. First, this scheme specifies a new data protection method by encrypting the data and randomizing it by performing XOR operation with a random bit-string. Second, this scheme can effectively protect data-in-transit against passive attack such as ciphertext analysis due to the randomization. Third, this scheme is lightweight and only requires a symmetric encryption scheme and bitwise operations, which requires processing time in the order of milliseconds.
暂无评论