+1. Staging it to gain experience, and starting with the GPU backend is the right approach.
Regards, Berthold Reinwald IBM Almaden Research Center office: (408) 927 2208; T/L: 457 2208 e-mail: reinw...@us.ibm.com From: Matthias Boehm <mboe...@googlemail.com> To: dev@systemml.apache.org Date: 10/24/2017 09:07 PM Subject: Re: [DISCUSS] Support for lower precision in SystemML +1 this is really great. I like the semantics of a best-effort use of single-precision (when configured we're free to use single precision but can fall back to double precision if certain operations or backends don't support it yet). This allows us to add single precision support incrementally one component at-a-time. For the java backends, this will entail creating a good internal abstraction and well, reimplementing most of the operations. Hence, it's probably a good idea to create an umbrella jira and add the individual design docs as we go. Regards, Matthias On Tue, Oct 24, 2017 at 8:26 AM, Niketan Pansare <npan...@us.ibm.com> wrote: > Hi all, > > We are in process of adding support for lower precision and wanted to give > everyone heads up. By lower precision, I mean support storing matrices in > float array (or half precision array) and performing operations using float > kernels. Initial experiments suggest that we can get up to 2x improvements > in terms of performance for Deep Learning algorithms. Also, this reduces > the memory requirements by 2x. > > Please provide any concerns or suggestions. > > The high-level plan is as follows: > 1. Support lower precision on GPU. Please see https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_&d=DwIBaQ&c=jf_iaSHvJObTbx-siA1ZOg&r=rKpXCxhAHRimLTQtp6EVnl3INiKbhTT17qTa33Vf73k&m=zWUKaXp5UoL0_2Ox7fxvfE2N6H37Pcy0xssylnuRwww&s=l7YQ4c3HAYq4nDXiuQkxWXkZr1NPxLN6c50WflVo9OY&e= > systemml/pull/688 > 2. Support lower precision with native BLAS. > 3. Support lower precision on CP/Spark. This includes writing float matrix > in binary format and updating memory estimation in hops. > 4. Extend Python APIs to support lower precision. > > The first two steps requires the conversion of double array to float/half > precision array. > > Thanks > > Niketan. >
