http://dx.doi.org/10.1002/cpe.1826">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Tsunami: Massively parallel homomorphic hashing on many-core GPUs

Language

English

Abstract

Homomorphic hash functions play a key role in securing distributed systems that use coding techniques such as erasure coding and network coding. The computational complexity of homomorphic hash functions remains a main challenge. In this paper, we present a massively parallel solution, named Tsunami, by exploiting the widely available many-core graphic processing units (GPUs). Tsunami includes the following optimization techniques to achieve the highest ever hashing throughput: (1) using Montgomery multiplication and precomputation to speed up modular exponentiations; (2) using a clean implementation of Montgomery multiplication in order to decrease the demand of registers and shared memory and increase the utilization ratio of GPU processing cores; (3) using our own assembly code to implement the 32-bit integer multiplication, which outperforms the assembly codes generated by the native compiler by 20%; and (4) exploiting memory alignment and constant memory on GPUs to improve the efficiency of memory access. Integrating the above techniques, our Tsunami achieves a significant improvement over existing results. Specifically, the hashing throughput achieved by Tsunami on a GTX295 GPU (NVIDIA, Santa Clara, CA, US) is about 33 times that of the existing solution on a quad-core CPU. We also show that the hashing throughput grows almost linearly with the number of GPU cores. Copyright © 2011 John Wiley & Sons, Ltd.

Keywords

CUDA, GPU, homomorphic hash function

Publication Date

2012

Source Publication Title

Concurrency and Computation: Practice and Experience

Volume

24

Issue

17

Start Page

2028

End Page

2039

Publisher

Wiley

ISSN (print)

15320626

ISSN (electronic)

15320634

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