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As I know the symmetric encryption algorithms are faster than asymmetric encryption algorithms. But when I test GCM AES-128 and Curve25519 encryption time, I find Curve25519 is faster than GCM AES-128.
Is that normal?
Is GCM AES-128 slow?

By the way, I use these python libraries for the comparison:

I performed the tests with Ubuntu 16.04 64-bit on an Intel Core i7-2640M and ran the tests multiple times with differing input lengths.

The speeds for curve25519 are (1 kB):
Encryption 6.29425048828e-05 sec
Decryption 2.31266021729e-05 sec

The speeds for GCM are (1 kB):
Encryption 0.000115156173706 sec
Decryption 0.000576972961426 sec
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  • $\begingroup$ The link is to NaCl which uses Salsa20 and Poly1305 for authenticated encryption. $\endgroup$
    – Maarten Bodewes
    Commented Aug 18, 2018 at 18:58
  • $\begingroup$ It appears that the AES-GCM library uses OpenSSL for AES-GCM, which means hardware support should be enabled... $\endgroup$
    – SEJPM
    Commented Aug 18, 2018 at 19:16
  • $\begingroup$ So my feeling is that AES-NI and / or CLMUL (hardware features of your CPU to accelerate AES-GCM) are not properly utilized here. However I couldn't confirm it looking at the code. Can you perhaps post the speeds you measured on your machine for both algorithms? $\endgroup$
    – SEJPM
    Commented Aug 18, 2018 at 19:29

1 Answer 1

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You are not just using Curve25519. You are using a Python wrapper to NaCl, which is a high level crypto library that uses Curve25519 for Diffie-Hellman computation to derive a secret value. This secret is then used to encrypt with a particular algorithm, defaulting to Salsa20/Poly1305. So this library performs hybrid cryptography, where the data itself is encrypted with a fast, authenticated stream cipher. Although functionally the API is high level, execution certainly isn't: it's a highly focused, minimal implementation that is focused on both speed and security.

The cryptography library on the other part uses OpenSSL to speed up AES-GCM implementations. If a high enough OpenSSL library is used then AES-NI as well as CMUL for the GMAC operations should be available on high end CPU's that support it. However, it is completely possible to use an older library or one that isn't compiled for AES-NI. OpenSSL is an old generic library that may also have some more overhead; it isn't targeted forspeed in the same way that NaCL is.

If you have larger messages then the speed difference is simply due to speed differences in implementation of Salsa20/Poly1305 and AES in GCM mode. Salsa20 is a fast stream cipher, while GCM mode can generally be accelerated by hardware.


Beware that testing for speed is particularly tricky, especially on higher level languages. Differences in startup time of the compiler parser may already influence the results. You need a loop for the startup time and a loop for the testing. Both loops should contain the algorithm.

Also note that the random number generation may be pretty different on each platform.

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  • $\begingroup$ Happy to upvote answers that contain more specific knowledge about the difference. Unfortunately, doing the testing myself is going to be a bit too arduous. $\endgroup$
    – Maarten Bodewes
    Commented Aug 18, 2018 at 19:52
  • $\begingroup$ So it turns out that a) OP probably used python which compiles to bytecode and then only interprets said bytecode (and doesn't actually JIT compile it to machine code) and b) the variance on these measurements is probably quite extreme especially if you consider that the AES-GCM decryption took 5 times as much time as the encryption, even though it's (nearly) the exact same operations that need to be performed. $\endgroup$
    – SEJPM
    Commented Aug 18, 2018 at 20:04
  • $\begingroup$ Yeah, the differences are pretty small. I do think that NaCl uses less instructions to achieve the same thing; OpenSSL has a lot of configurations (engine choices, specialized memory management etc.) but even simply calling the random number generator could already explain the differences for such small tests. $\endgroup$
    – Maarten Bodewes
    Commented Aug 18, 2018 at 20:50

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