9 ms·

Unless it's secure, I see no motivation to use it. In their table, they acknowledge that Arc4Random and ChaCha20 as secure. The only negatives against ChaCha20

Unless it's secure, I see no motivation to use it. In their table, they acknowledge that Arc4Random and ChaCha20 as secure. The only negatives against ChaCha20 is that it's not 'fast enough' and k-Dimensional Equidistribution. In my experience it's never showed up in my profiling, so I feel it's fast enough; and I'm not sure why I want k-Dimensional Equidistribution. So... I'll keeping using ChaCha20 when I need to (it's part of nacl/libsodium if you don't know where to get it from).

nightcracker 11y ago
I wrote a standalone C++11 (compatible with the standard RNG library) implementation of ChaCha (with the number of rounds as a parameter), available here: https://gist.github.com/orlp/32f5d1b631ab092608b1 https://gist.github.com/orlp/32f5d1b631ab092608b1 This is also the implementation used for the benchmark at the pcg-random.org homepage.
- daurnimator 11y ago
  How does it compare to the libsodium implementation: https://github.com/jedisct1/libsodium/blob/master/src/libsodium/crypto_stream/chacha20/ref/stream_chacha20_ref.c https://github.com/jedisct1/libsodium/blob/master/src/libsod... Btw, libsodium prefers salsa20 instead of chacha20, I don't know why; but I trust they made a good decision.
  - nightcracker 11y ago
    It's essentially the same, except for the API. However, you linked to the reference implementation. My implementation also contains a SIMD implementation, if your CPU supports it. I assume that libsodium has a similar implementation in its codebase somewhere too. My SIMD implementation can be made to run a bit faster, but this would mean computing more blocks in parallel, and thus increase the state size of the RNG even more. I chose to only compute one block at a time. An optimized stream cipher implementation will have all the implementations with different parellel block sizes embedded, to achieve optimal speed for long messages. --- ChaCha20 is a stronger more modern variation on Salsa20. But the differences are minor. But, to be precise, libsodium prefers XSalsa20 to ChaCha20. The core feature about XSalsa20 is that it supports a 192-bit nonce instead of the 64-bit nonce of ChaCha20. This means that it is safe to use a randomly generated nonce, while randomly generating a 64-bit nonce would result in collisions eventually. I believe this is the reason they chose XSalsa20. XSalsa20 is a simple variation on Salsa20 that does a bit more initialization to turn the normal 64-bit nonce into 192-bit. I assume the same construction can be applied to ChaCha20, but since djb (the author of both Salsa, XSalsa and ChaCha) hasn't done it, I think that libsodium went with the safe approach and just used XSalsa20.
    - pbsd 11y ago
      'A bit faster' is an understatement. On the benchmarking machine used by the PCG author, you would get a ~2.5x speedup over that implementation, which would put Chacha20 roughly at (amortized) 5 cycles per 32-bit word. If you go Chacha8, which is still cryptographically secure, that becomes 2-3 amortized cycles per word. The flagship PCG generator from the paper, 'PCG XSL RR RR 128', runs at 1.81 nanoseconds per word on the same machine. This converts to ~6 cycles per word. So I am not inclined to believe the claim, right on the frontpage, that Chacha20 is 'fairly slow' . Unless you're hurting for space---and in a Haswell machine you probably aren't---the case for using a cryptographic generator everywhere is strong. If you're hurting for space, or are on a less desktop-oriented architecture, you probably are not going to like 64-bit integer multiplications and variable rotation counts either. By the way, you're using the aligned _mm_load_si128 and _mm_store_si128 intrinsics to load and store the block: https://gist.github.com/orlp/32f5d1b631ab092608b1#file-chacha-h-L163 https://gist.github.com/orlp/32f5d1b631ab092608b1#file-chach.... But there's no guarantee that block is aligned to 16 bytes, so that code may crash in some compiler/platform combination.
      - nightcracker 11y ago
        The fastest ChaCha20 implementation available (from http://bench.cr.yp.to/results-stream.html http://bench.cr.yp.to/results-stream.html) on a modern Intel processor is 1.2 cycles / byte (cpb) for ChaCha20 and 0.6 cpb for ChaCha8. ChaCha is very fast. The drawback however, is that these implementation only get that fast because ChaCha is embarrassingly parallel. The fastest implementations can be computing 16-24 blocks in parallel efficiently with AVX2. That's 1.5kB of random data generated at once. And it's only this fast in a closed loop, where all code and memory is hot. For the above reasons and simplicity I've chosen to not do a parallel SIMD implementation in my gist. But I agree that 'fairly slow' is not really fair to say, at least not in combination with 'Good' statistical quality (rather than Excellent, because the author found faults in ChaCha2). ChaCha8 is blazing fast, and so far no cryptographer has been able to do a successful attack against it. However, before, ChaCha was mentioned as being 'slow', and I attempted to fix that by emailing the author with my implementation. - Interestingly, I was (sadly I never got to finish it before the http://competitions.cr.yp.to/caesar.html http://competitions.cr.yp.to/caesar.html deadline) working on an AEAD design similar to ChaCha, that was doing 1.5 cpb authenticated encryption on AVX2: http://www.liacs.nl/~opeters/orlein.pdf http://www.liacs.nl/~opeters/orlein.pdf. SIMD plus embarrassingly parallel ciphers is a strong combination. - Thanks for the warning about the alignment. Because I tacked on the SIMD implementation later, I forgot to add the alignment requirement, I did that now.
- netheril96 11y ago
  What is the stream parameter in the constructor `explicit ChaCha(uint64_t seedval, uint64_t stream = 0);`?
  - nightcracker 11y ago
    It allows you to make multiple parallel streams of random data with the same seed. If you prefer, you can see the seed + stream as a combined 128 bit seed.
zaroth 11y ago
> I'm not sure why I want k-Dimensional Equidistribution For the party tricks, of course! http://www.pcg-random.org/party-tricks.html http://www.pcg-random.org/party-tricks.html
deleted 11y ago
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bradleyjg 11y ago
Just as you wouldn't want to use a general purpose PRNG for security applications so too you wouldn't want to use a secure PRNG for general purposes, particularly demanding applications such as Monte Carlo simulations. Your dismissal strikes me as both ignorant and rude.
- JoachimS 11y ago
  What are the random number data rates for those applications? One of the good features of ChaCha is that it is fast. A modern CPU can churn out Gbps of data. And in HW you can do it at low clock frequencies. This is the reason we use ChaCha as the CSPRNG in the Cryptech HSM.
- daurnimator 11y ago
  > Just as you wouldn't want to use a general purpose PRNG for security applications so too you wouldn't want to use a secure PRNG for general purposes, particularly demanding applications such as Monte Carlo simulations. Why not? It's perfectly valid to use a secure RNG for non-secure purposes. As I mentioned, I've used ChaCha20 for those sort of purposes too. And the random number generation barely shows up as a fraction of the runtime. i.e. improving 1% of the runtime by 99% isn't worth the overhead of having more than one random number generator.
  - danieltillett 11y ago
    The reason why is crypto RNG are much slower than something less secure. It really depends on the application, but in the code I wrote today a significant fraction of my application’s time was spent in the RNG function. By replacing the RNG I managed to speed up my code by a bit over 2 fold. I am not doing crypto work so this is great.
    - daurnimator 11y ago
      ChaCha is fast. very fast. see just below: https://news.ycombinator.com/item?id=9890599 https://news.ycombinator.com/item?id=9890599 I'm not sure why they marked it as "fairly slow" in the table in the OP.
slavik81 11y ago
What's your application? When I wrote a ray tracer that did photon mapping, I needed about 300 000 000 random numbers per light to get half-decent results. Admittedly, I could have done more to optimize my use of those numbers, but the point remains that you need huge numbers of random samples. I used mt19937 for this and it worked ok. I suspect that using pcg might have shaved a few percentage points off the total run time of the program (maybe I should do a benchmark sometime). If so, not a huge win, but noticeable. Especially if you're doing renders on a larger scale than I was. A couple percentage points could trim a few computers from a render farm.

nightcracker 11y ago

I wrote a standalone C++11 (compatible with the standard RNG library) implementation of ChaCha (with the number of rounds as a parameter), available here: https://gist.github.com/orlp/32f5d1b631ab092608b1 https://gist.github.com/orlp/32f5d1b631ab092608b1 This is also the implementation used for the benchmark at the pcg-random.org homepage.

daurnimator 11y ago
How does it compare to the libsodium implementation: https://github.com/jedisct1/libsodium/blob/master/src/libsodium/crypto_stream/chacha20/ref/stream_chacha20_ref.c https://github.com/jedisct1/libsodium/blob/master/src/libsod... Btw, libsodium prefers salsa20 instead of chacha20, I don't know why; but I trust they made a good decision.
- nightcracker 11y ago
  It's essentially the same, except for the API. However, you linked to the reference implementation. My implementation also contains a SIMD implementation, if your CPU supports it. I assume that libsodium has a similar implementation in its codebase somewhere too. My SIMD implementation can be made to run a bit faster, but this would mean computing more blocks in parallel, and thus increase the state size of the RNG even more. I chose to only compute one block at a time. An optimized stream cipher implementation will have all the implementations with different parellel block sizes embedded, to achieve optimal speed for long messages. --- ChaCha20 is a stronger more modern variation on Salsa20. But the differences are minor. But, to be precise, libsodium prefers XSalsa20 to ChaCha20. The core feature about XSalsa20 is that it supports a 192-bit nonce instead of the 64-bit nonce of ChaCha20. This means that it is safe to use a randomly generated nonce, while randomly generating a 64-bit nonce would result in collisions eventually. I believe this is the reason they chose XSalsa20. XSalsa20 is a simple variation on Salsa20 that does a bit more initialization to turn the normal 64-bit nonce into 192-bit. I assume the same construction can be applied to ChaCha20, but since djb (the author of both Salsa, XSalsa and ChaCha) hasn't done it, I think that libsodium went with the safe approach and just used XSalsa20.
  - pbsd 11y ago
    'A bit faster' is an understatement. On the benchmarking machine used by the PCG author, you would get a ~2.5x speedup over that implementation, which would put Chacha20 roughly at (amortized) 5 cycles per 32-bit word. If you go Chacha8, which is still cryptographically secure, that becomes 2-3 amortized cycles per word. The flagship PCG generator from the paper, 'PCG XSL RR RR 128', runs at 1.81 nanoseconds per word on the same machine. This converts to ~6 cycles per word. So I am not inclined to believe the claim, right on the frontpage, that Chacha20 is 'fairly slow' . Unless you're hurting for space---and in a Haswell machine you probably aren't---the case for using a cryptographic generator everywhere is strong. If you're hurting for space, or are on a less desktop-oriented architecture, you probably are not going to like 64-bit integer multiplications and variable rotation counts either. By the way, you're using the aligned _mm_load_si128 and _mm_store_si128 intrinsics to load and store the block: https://gist.github.com/orlp/32f5d1b631ab092608b1#file-chacha-h-L163 https://gist.github.com/orlp/32f5d1b631ab092608b1#file-chach.... But there's no guarantee that block is aligned to 16 bytes, so that code may crash in some compiler/platform combination.
    - nightcracker 11y ago
      The fastest ChaCha20 implementation available (from http://bench.cr.yp.to/results-stream.html http://bench.cr.yp.to/results-stream.html) on a modern Intel processor is 1.2 cycles / byte (cpb) for ChaCha20 and 0.6 cpb for ChaCha8. ChaCha is very fast. The drawback however, is that these implementation only get that fast because ChaCha is embarrassingly parallel. The fastest implementations can be computing 16-24 blocks in parallel efficiently with AVX2. That's 1.5kB of random data generated at once. And it's only this fast in a closed loop, where all code and memory is hot. For the above reasons and simplicity I've chosen to not do a parallel SIMD implementation in my gist. But I agree that 'fairly slow' is not really fair to say, at least not in combination with 'Good' statistical quality (rather than Excellent, because the author found faults in ChaCha2). ChaCha8 is blazing fast, and so far no cryptographer has been able to do a successful attack against it. However, before, ChaCha was mentioned as being 'slow', and I attempted to fix that by emailing the author with my implementation. - Interestingly, I was (sadly I never got to finish it before the http://competitions.cr.yp.to/caesar.html http://competitions.cr.yp.to/caesar.html deadline) working on an AEAD design similar to ChaCha, that was doing 1.5 cpb authenticated encryption on AVX2: http://www.liacs.nl/~opeters/orlein.pdf http://www.liacs.nl/~opeters/orlein.pdf. SIMD plus embarrassingly parallel ciphers is a strong combination. - Thanks for the warning about the alignment. Because I tacked on the SIMD implementation later, I forgot to add the alignment requirement, I did that now.
netheril96 11y ago
What is the stream parameter in the constructor `explicit ChaCha(uint64_t seedval, uint64_t stream = 0);`?
- nightcracker 11y ago
  It allows you to make multiple parallel streams of random data with the same seed. If you prefer, you can see the seed + stream as a combined 128 bit seed.

zaroth 11y ago

> I'm not sure why I want k-Dimensional Equidistribution For the party tricks, of course! http://www.pcg-random.org/party-tricks.html http://www.pcg-random.org/party-tricks.html

deleted 11y ago

[deleted]

bradleyjg 11y ago

Just as you wouldn't want to use a general purpose PRNG for security applications so too you wouldn't want to use a secure PRNG for general purposes, particularly demanding applications such as Monte Carlo simulations. Your dismissal strikes me as both ignorant and rude.

JoachimS 11y ago
What are the random number data rates for those applications? One of the good features of ChaCha is that it is fast. A modern CPU can churn out Gbps of data. And in HW you can do it at low clock frequencies. This is the reason we use ChaCha as the CSPRNG in the Cryptech HSM.
daurnimator 11y ago
> Just as you wouldn't want to use a general purpose PRNG for security applications so too you wouldn't want to use a secure PRNG for general purposes, particularly demanding applications such as Monte Carlo simulations. Why not? It's perfectly valid to use a secure RNG for non-secure purposes. As I mentioned, I've used ChaCha20 for those sort of purposes too. And the random number generation barely shows up as a fraction of the runtime. i.e. improving 1% of the runtime by 99% isn't worth the overhead of having more than one random number generator.
- danieltillett 11y ago
  The reason why is crypto RNG are much slower than something less secure. It really depends on the application, but in the code I wrote today a significant fraction of my application’s time was spent in the RNG function. By replacing the RNG I managed to speed up my code by a bit over 2 fold. I am not doing crypto work so this is great.
  - daurnimator 11y ago
    ChaCha is fast. very fast. see just below: https://news.ycombinator.com/item?id=9890599 https://news.ycombinator.com/item?id=9890599 I'm not sure why they marked it as "fairly slow" in the table in the OP.

slavik81 11y ago

What's your application? When I wrote a ray tracer that did photon mapping, I needed about 300 000 000 random numbers per light to get half-decent results. Admittedly, I could have done more to optimize my use of those numbers, but the point remains that you need huge numbers of random samples. I used mt19937 for this and it worked ok. I suspect that using pcg might have shaved a few percentage points off the total run time of the program (maybe I should do a benchmark sometime). If so, not a huge win, but noticeable. Especially if you're doing renders on a larger scale than I was. A couple percentage points could trim a few computers from a render farm.