Native Tensor support

Isiah Meadows isiahmeadows at
Sat Jan 27 03:49:51 UTC 2018

Two questions:

1. Would APIs that operate on existing data types (rather than tensors)
work just as well?
2. Could there instead be a kernel-like API that could work on things
independently? Lower level APIs that enable equivalent high-level
constructs is a much better place to start.

And two comments:

1. You could easily emulate 2D arrays by simply using 1D arrays and storing
data in row- or column-major order. This is pretty well-known at this
point, and is how C/C++ allocate multi-dimensional arrays internally.
2. Data parallelization requires special consideration, and I can assure
you, machine learning isn't the only thing that could stand to benefit from
this. It needs to be broad enpugh that other non-data applications can
benefit from it. (Applying DOM changes from a static change list is an
embarassingly parallel\* problem. So anything that could speed this up by a
substantial bit could be infinitely useful to anyone using Angular, React,
Ember, or any other framework out there.)

\* Yes, that's a technical term. Look it up on Wikipedia.

On Fri, Jan 26, 2018, 20:50 Robert Eisele <robert at> wrote:

> Hello,
> Allocating multi-dimensional arrays in Javascript is only possible by
> building each dimension individually. In addition to being a very
> tedious job, a developer has no control over memory usage, which in
> general is likely to be very high.
> Seeing an array algebraically as a vector, typed arrays have already
> created the ability to work more efficiently and memory-consciously with
> lists of numbers. A natural extension of this is not just a matrix, but
> a tensor.
> I would like to suggest tensors as a native language construct in ES.
> This would have the advantage that developers could write highly
> parallelizable code independently of WebGL. As an API one could
> introduce the following classes in analogy to typed arrays:
> - IntXTensor
> - UintXTensor
> - FloatXTensor
> Where X is one of {8, 16, 32, 64}. To make these tensor objects really
> effective, it is necessary to introduce meaningful operations, maybe
> similar to the features of TensorFlow. I think by introducing tensors in
> the browser (but also node.js), a wide range of new applications open
> up. For example, working with deep learning right in the browser or
> calculating filters on images without having to write shaders for them.
> The most important thing probably is having a way of storing high
> dimensional data in the browser without worrying about the memory
> footprint, even for complex applications.
> What do you think about it?
> Robert Eisele
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