On Thu., 12 Dec. 2019, 10:03 pm Mark Shannon, <m...@hotpy.org> wrote:
> > Explicit limits are much easier to test. Does code outside the limit > fail in the expected fashion and code just under the limit work correctly? > > What I want, is to allow more efficient use of resources without > inconveniently low or unspecified limits. There will always be some > limits on finite machines. If they aren't specified, they still exist, > we just don't know what they are or how they will manifest themselves. > This is the main reason I'd like to see the PEP split into two distinct aspects: * articulating & testing specific limits (we already have some bigmem tests that can only be run at full scale on server class machines with 20+ GiB of RAM - don't forget that CPython runs on supercomputers, not just regular machines). * potentially lowering some specific limits in the CPython implementation The first part is clearly valuable as a general capability, especially if there's an API that lets different implementations report different limits. For example, something like a "sys.implementation.limits" informational API, modelled on the way that the top level "sys.implementation" data structure already works. Even when the limits are large, we have potential access to machines that can accommodate them for testing purposes (e.g. I know at least one Linux vendor that we're on good terms with that has servers with more than a terabyte of RAM in their test fleet, and I presume other OS vendors and cloud platform providers have similar capabilities). By contrast, the second part really needs to be tackled on a case-by-case basis, with a clear understanding of the kinds of failure modes we're aiming to handle more gracefully, which kinds of performance improvements we're aiming to enable, and which existing capabilities we'd be reducing. Cheers, Nick. >
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