laser
JohnTheRipper
| laser | JohnTheRipper | |
|---|---|---|
| 6 | 5 | |
| 261 | 4,811 | |
| 1.5% | - | |
| 3.6 | 0.0 | |
| 4 months ago | about 3 years ago | |
| Nim | C | |
| Apache License 2.0 | - |
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laser
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From slow to SIMD: A Go optimization story
It depends.
You need 2~3 accumulators to saturate instruction-level parallelism with a parallel sum reduction. But the compiler won't do it because it only creates those when the operation is associative, i.e. (a+b)+c = a+(b+c), which is true for integers but not for floats.
There is an escape hatch in -ffast-math.
I have extensive benches on this here: https://github.com/mratsim/laser/blob/master/benchmarks%2Ffp...
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Benchmarking 20 programming languages on N-queens and matrix multiplication
Ah,
It was from an older implementation that wasn't compatible with Nim v2. I've commented it out.
If you pull again it should work.
> Anyway the reason for your competitive performance is likely that you are benchmarking with very small matrices. OpenBLAS spends some time preprocessing the tiles which doesn't really pay off until they become really huge.
I don't get why you think it's impossible to reach BLAS speed. The matrix sizes are configured here: https://github.com/mratsim/laser/blob/master/benchmarks/gemm...
It defaults to 1920x1920 * 1920x1920. Note, if you activate the benchmarks versus PyTorch Glow, in the past it didn't support non-multiple of 16 or something, not sure today.
Packing is done here: https://github.com/mratsim/laser/blob/master/laser/primitive...
And it also support pre-packing which is useful to reimplement batch_matmul like what CuBLAS provides and is quite useful for convolution via matmul.
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Why does working with a transposed tensor not make the following operations less performant?
For convolutions: - https://github.com/numforge/laser/blob/e23b5d63/research/convolution_optimisation_resources.md
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Improve performance with SIMD intrinsics
You can train yourself on matrix transposition first. It's straightforward to get 3x speedup between naive transposition and double loop tiling, see: https://github.com/numforge/laser/blob/d1e6ae6/benchmarks/transpose/transpose_bench.nim#L238
JohnTheRipper
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command zip2john not found help
git clone "https://github.com/magnumripper/JohnTheRipper.git" && cd JohnTheRipper/src && ./configure && sudo make -s clean && sudo make -sj4
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PDF encryption/decryption
Use the pdf2john.pl command (part of the John the Ripper magnum pack) to create a hash for each pdf file.
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Awesome CTF : Top Learning Resource Labs
John The Jumbo - Community enhanced version of John the Ripper.
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Multidoge - Private Key Cannot be imported, can't decrypt.
To try this you need, time, the original python, (not python3, which wont work) Then you need to get the python script file named multibit2john.py
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Password strength increase from repeating sequence?
John the Ripper Jumbo has rules to:
What are some alternatives?
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
hashcat - World's fastest and most advanced password recovery utility
nim-sos - Nim wrapper for Sandia-OpenSHMEM
john - John the Ripper jumbo - advanced offline password cracker, which supports hundreds of hash and cipher types, and runs on many operating systems, CPUs, GPUs, and even some FPGAs
ParallelReductionsBenchmark - Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast!
bitcracker - BitCracker is the first open source password cracking tool for memory units encrypted with BitLocker
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
blake3 - An AVX-512 accelerated implementation of the BLAKE3 cryptographic hash function
blis - BLAS-like Library Instantiation Software Framework
42_CheatSheet - A comprehensive guide to 50 years of evolution of strict C programming, a tribute to Dennis Ritchie's language
lm8 - A custom 8-bit computer and software suite