fast watermarking for digital content protecting using gpus

Digital WatermarkingParallel Programming Model

OptimizationImplementation

Fast Watermarking for Digital Content Protectingusing GPUs

Amirali Sharifian

Isfahan University of Technology

February 19, 2014

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Amirali Sharifian Fast Watermarking for Digital Content Protecting using GPUs



1 Digital WatermarkingIntroductionDCT

2 Parallel Programming ModelHardwareProgramming Model

3 OptimizationBlock SizeTiled algorithm

4 ImplementationResultsFuture implementation

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Questions

What is watermarking and why we use it?

How we model our programs on GPUs and what is CUDA C?

What is the role and importance of GPUs in watermarking?

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Watermark

Def: A watermark is a recognizableimage or pattern in paper that appearsas various shades of lightness/darkness

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Digital Watermarking

Def: The procedure of embedding copyright information in adigital content such as image, video or audio.

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Watermarking system methods and properties

Spatial domain

Frequency domain

- DFT- DCT- DWT

Spatial domain Frequency domainComputation complexity Low High

Robustness Fragile More robust

Capacity High Low

Application Authentication Copyright

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Watermarking method

Image(Cover)

Partitioning DCT Embeding

IDCT ReconstructingI ′

Watermarked Image

Bi DCT (Bi)

DCT ′(Bi)

B′i

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Discrete Cosine TransformDefinition method

Definition method

C(U,V ) = α(u)α(v)∑N−1

x=0

∑N−1y=0 f (x , y) cos

[π(2x+1)u

1N

]cos

[π(2y+1)v

2N

]

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Discrete Cosine TransformEfficient method

Efficient method

2-D DCT(x) = 1-D DCT((1-D DCT(X ))t)

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Embedding method

λ = |Ck(L)− Ck(S)|

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Architecture

CPU and GPU are designed very differently

CPUs: Latency oriented design

Powerful ALU

Large caches

Sophisticated control

GPUs: Throughput oriented design

Small Chaches

Simple control

Energy efficient ALUs

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CUDA / OpenCL - Execution model

Heterogeneous host+device application C program- Serial parts in host C code- Parallel parts in device SPMD kernel C code

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A Multi-Dimensional Grid Example

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CUDA memory model

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” Parallel programming is easy as long as you don’t care aboutperformance.”

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Executing thread blocks

Threads are assigned to StreamingMultiprocessors (SM) in blockgranularity

Up to 8 Blocks to each SM asresource allowsFermi SM can take up to 1536threads

Could be 256 (threads/block) * 6blocksOr 512 (threads/block) * 3blocks, etc.

Each block is executed as 32-threadWarps

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The Von-NeumannModel

The Von-NeumannModel with SIMD

units

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Importance of memory access efficiently

Compute to Global Memory Access (CGMA)- Memory bandwidth = 200GB/s → 50(200/4) giga

single-percision → 50 GFLOPS- The peak single-precision performance: 1,500 GFLOPS !!

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Tiled matrix multiplication

Global memory access pattern of the base matrix multiplicationkernel

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Tiled matrix multiplicationOptimized

For 150GB/s global memory bandwidth⇒ (150/4)× 16 = 600 GFLOPS !!

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Other Optimization

Dram bandwidth

Memory coalescing

Tiled Convolution

Data transfer and Virtual Memory

Pinned memory and DMA data transfer

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Speedup

Implementation results on a system with Core i7 - 3.40 GHZ CPUand GTX570 GPU:

Speedup = GPUTimeCPUTime

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Comparing two different systems

System 1 System 2

CPU Core i7 - 3.40 GHZ Core i3 - 3.10 GHZ

GPU GTX 570 GTX 460

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Implementing the algorithm on mobile phones

Using watermarking algorithms in multimedia streaming

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Question ?

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