CUDA Compatible GPU as an Efficient Hardware Accelerator for AES Cryptography

By Svetlin Manavski. 2007 IEEE International Conference on Signal Processing and Communications, 2007

359 citations recorded by the source corpus.

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Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, methodology): “plications include bitmap indices [160, 161, 162, 163] and bitwise scan acceleration [164] for databases, accelerated document filtering for web search [165], DNA sequence alignment [166, 167, 168], encryption algorithms [169, 170, 171], graph processing [78], and networking [159].”
Analyzing CUDA workloads using a detailed GPU simulator (2009, methodology): “While the authors of the CUDA applications which we use as benchmarks have published work, the emphasis of their papers was not on how changes in the GPU architecture can affect their applications [4, 5, 12, 13, 15, 24, 26, 27, 37, 41, 42, 46].”
PacketShader: a GPU-accelerated software router (2010, background): “Recently, GPUs have shown a substantial performance boost to many network-related workloads, including pattern matching [35, 48, 53], network coding [47], IP table lookup [35], and cryptography [24,32,49].”
Ambit: In-Memory Accelerator for Bulk Bitwise Operations Using Commodity DRAM Technology (2017, background): “Many encryption algorithms heavily use bitwise operations (e.g., XOR) [44, 83, 107].”