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History• Ian Buck, Dir. of GPU Computing, received his PhD from Stanford for his research on GPPM in 2004• Started working for Nvidia to commercialize GPU computing• First start was in 2006, Nvidia released CUDA v 1.0 for G80• In spring 2008, CUDA 2.0 was released together with GT200

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About• With CUDA, normal applications can be ported to GPU for higher performance• No low level or 3D programming knowledge required, CUDA works with C

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CPU vs GPU• A CPU core can execute 4 32-bit instructions per clock, whilst a GPU can execute 3200 32-bit instructions per clock• A CPU is designed primarily to be an executive and make decisions• A GPU is different, it has a large number of ALU’s(Arithmetic/Logic Units), a lot more than a CPU.

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Structure• In CUDA, you are required to specify the number of blocks and threads in each block.• One block can contain up to 512 threads.• Each thread on each block is executed separately.

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Structure

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Syntax• Key parts:• Identifying a GPU function (__global__, __device__)• Calling a GPU function, specifying number of blocks and threads per block function<<<block_nr, thread_nr>>>(param);

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Syntax• CPU Code:• Calling function:

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Syntax• GPU Code:• Calling function:

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Bruteforce• As a lot of information is processed at the same time, parallel programming has a big impact on bruteforce• Number of tries increases drastically on a GPU than on a CPU

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Examples• Let’s say we have a password to break, and the only thing we know is it has length=3• A simple bruteforce would be:

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Examples• A GPU bruteforce:• Called like this:

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Examples• A more efficient GPU bruteforce:• Called like this:

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Real Life• Let’s say we have an MD5 and a wordlist of 1.000.000 words• A simple bruteforce would be:

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Real Life• A GPU bruteforce would be:• Called like this:• threadIdx.x+blockIdx.x*blockDim.x is the thread ID (ranging from 1 to 1.000.000)• 2000*500=1.000.000 threads