I’m looking for a simple formula for the disk/memory usage for CCSD(T) calculations, as I’m planning to run some very large calculations and might need to get a bigger disk. I see formulas to calculate this for DF-CCSD(T) in the source code but couldn’t find anything for conventional CCSD(T). Is there any such formula someone could direct me to?

Using Psi’s conventional CCSD(T) is a terrible, terrible idea. The (T) correction takes far longer than it needs to. Have you considered using CFOUR? Assuming you want closed-shell species, their CCSD(T) using the NCC module is amazingly fast.

A bit more context… I’m trying to do some atomization energies, where the atoms can’t be done with DF-CCSD(T). The largest calculation has 1300 basis functions, but the rest should be pretty easy… so I don’t really mind. I just want to know how much disk space I’ll need.

Assuming you ran some CCSD(T) already? It will print the integrals types it generates, using the default module. Also the disk usage.

The usually largest and dominating one is <VV|VV>, ie V=virtuals, O=occupied.
Perhaps also include <OV|VV>.

so ints=(v^4 + v^3*o)*8/(1024^2) MiB, would perhaps be a quick simple check for the CCSD integrals.

I think the fnocc module is overall faster. If you exceed 800 virtuals…good luck.
Optionally think about using FNO approximation, even very conservative with OCC_TOLERANCE=1e-7.

The conventional RHF-based (T) correction in PSI4 is based on the Rendell and Lee formulation which is very efficient. We use a minimum number of v^3 arrays and a minimum number of sorts between. In addition, Rollin King added threading to the triples algorithms, which gives one further opportunities for speed. In addition, at one time our code was slightly faster than that in CFOUR, but I’ve not made a direct comparison with their latest implementation. I can certainly believe that additional optimizations are warranted, but I have to take issue with the sentiment behind your comment. Can you explain what you mean by “takes far longer than it needs to”?

Thanks. I still have one point of confusion though…

In the output I see the line:
Size of irrep 0 of <ab|cd> integrals: 3154.957 (MW) / 25239.652 (MB)
Which matches the simple formula 8*v^4/1000^2.

However the file written to disk (psi.436384.mol.103) is taking up 36G of space. <ia|bc> is only 1064.964MB, so nothing else should be comparable to <VV|VV> right? I don’t see what I’m missing…

The fnocc module is a bit different because it splits its files more. But similar disk usage in the end. Though it seems the 2-e AO integrals from the SCF are not deleted, which could be sizable (not 100% sure on this)

For RHF-CC calculations, file 103 includes the <ab|cd> integrals (with no index packing), but it also includes two linear combinations that are packed: <ab|cd> + <ab|dc> and <ab|cd> - <ab|dc>. (These are used to streamline the particle-particle ladder terms in the various amplitude and EOM-CC equations.) For these two tensors, the row and column indices have dimension V*(V+1)/2, so the total word sizes of each tensor are (V*(V+1)/2)^2. If you have 237 virtual orbitals, then each of these tensors requires about 6.3 GB, which would account for your observation of a 36 GB file 103.

Sure, have an example from my current project. I can try the exact same CCSD(T) computation in Psi4 and CFOUR. Psi says (T) takes 1326 seconds walltime beyond CCSD. CFOUR’s ECC and NCC modules finish just a minute apart from each other. I don’t believe ECC’s reported (T) timing, but NCC reports 881 walltime seconds. Psi’s cc module RHF (T) computation takes an extra 50% walltime compared to CFOUR’s (T).

Are you taking full advantage of optimizations in PSI4’s implementation (including threading)? Is this an apples-to-apples comparison?

If it is, then we should try to learn what they’re doing to make their code faster. Perhaps they’re keeping more of their tensors in core (something we can also do in PSI4, but isn’t the default), thus reducing I/O wait times, or perhaps they’ve got a better threading scheme.

If it’s not a fair comparison, then we should adjust our default parameters to improve our performance on your hardware. In which case, it wouldn’t be a “terrible, terrible idea” to use PSI4.

As best as we can tell, it’s apples to apples. I ran this on the CCQC cluster, which Jet set up to allow threading on Psi. My CFOUR output says

Running on 4 MPI processes
Running with 1 threads/proc
Memory limit is: 6.51926GB
One-particle lists are cached
Two-particle lists are cached
T1 and T2 DIIS vectors are cached
ABCI is not cached
ABCD is done in the AO basis
ABC and ABCD transposes are coarse-threaded
An out-of-core algorithm is used for <Ab|Ci>
DIIS is used to accelerate convergence of T1 and T2

Psi4 output says

AO Basis = NONE
ABCD = NEW
Cache Level = 2
Cache Type = LOW

and

Number of threads for explicit ijk threading: 4
MKL num_threads set to 1 for explicit threading.

The time falls from 43 to 36 minutes upon boosting Psi’s cache level to 3, but the difference between cache levels 2 and 3 is the ABCI, which CFOUR says it doesn’t cache. So that comparison is better for Psi but is no longer apples to apples.

I would say that “apples to apples” is to use the highest level of optimization that each program can provide. Just because CFOUR doesn’t cache ABCI doesn’t mean you shouldn’t let PSI4 cache them because it can.