SCscottlegrand2·Nov 1, 2017
Not entirely true. Sparse x Dense Matrix is an important component of the DSSTNE deep learning framework:
https://github.com/amzn/amazon-dsstne
But this library is clearly more thorough.
In The Tensor Algebra Compileramazon-archives
amazon-dsstne
C++2016–2017
amazon-archives / amazon-dsstne · Archived
amazon-dsstne
Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models
8Distinct people
9Mentions
0Explicit recommenders
4.4KGitHub stars
What to know before exploring
What it is
Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by month9 dated mentions
Observed mentions: 2016-05-11 00:26:08 UTC to 2017-11-01 20:20:34 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| May 2016 | 2016-05-01 00:00:00 UTC | 2016-06-01 00:00:00 UTC | 4 | 1 | 3 |
| Jun 2016 | 2016-06-01 00:00:00 UTC | 2016-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2016 | 2016-07-01 00:00:00 UTC | 2016-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2016 | 2016-08-01 00:00:00 UTC | 2016-09-01 00:00:00 UTC | 1 | 0 | 1 |
| Sep 2016 | 2016-09-01 00:00:00 UTC | 2016-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2016 | 2016-10-01 00:00:00 UTC | 2016-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2016 | 2016-11-01 00:00:00 UTC | 2016-12-01 00:00:00 UTC | 1 | 0 | 1 |
| Dec 2016 | 2016-12-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 0 | 1 |
| Jan 2017 | 2017-01-01 00:00:00 UTC | 2017-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2017 | 2017-02-01 00:00:00 UTC | 2017-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2017 | 2017-03-01 00:00:00 UTC | 2017-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2017 | 2017-04-01 00:00:00 UTC | 2017-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2017 | 2017-05-01 00:00:00 UTC | 2017-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2017 | 2017-06-01 00:00:00 UTC | 2017-07-01 00:00:00 UTC | 1 | 0 | 1 |
| Jul 2017 | 2017-07-01 00:00:00 UTC | 2017-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2017 | 2017-08-01 00:00:00 UTC | 2017-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2017 | 2017-09-01 00:00:00 UTC | 2017-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2017 | 2017-10-01 00:00:00 UTC | 2017-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2017 | 2017-11-01 00:00:00 UTC | 2017-12-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
9 sourcesOSSed last year, silly example...
https://github.com/amzn/amazon-dsstne
In Darpa Funds Development of New Type of ProcessorAmazon Personalization.
We use ML/Deep Learning for customer to product recommendations and product to product recommendations. For years we used only algorithms based on basic statistics but we've found places where the machine learned models out perform the simpler models.
Here is our blog post and related GitHub repo:
https://aws.amazon.com/blogs/big-data/generating-recommendat...
https://github.com/amznlabs/amazon-dsstne
If you are interested in this space, we're always hiring. Shoot me an email ($my_hn_username@amazon.com) or visit https://www.amazon.jobs/en/teams/personalization-and-recomme...
In Ask HN: Where is AI/ML actually adding value at your company?Amazon has been building technology based on ML&DL for over 20 years and has developed several frameworks. You must have missed the announcement of this open source framework earlier in the year: https://github.com/amznlabs/amazon-dsstne.
In MXNet – Deep Learning Framework of Choice at AWSExcept that up to now at least, CUDA IMO remains the best abstraction for programming multi-core: subsuming away multiple threads, SIMD width, and multiple cores into the language definition. AMBER (http://www.ambermd.org) literally "recompiled and ran" with each succeeding GPU generation since GTX 280 in 2009. 3-5 days of subsequent refactoring then unlocked 80% of the attainable performance gains of each of the subsequent GPUs. DSSTNE (https://github.com/amznlabs/amazon-dsstne) just ran as well, but it's only targeting Kepler and up because the code relies heavily on the __shfl instruction.
So I honestly don't get the Google clang CUDA compiler right now. It's really really cool work, but I don't get why they didn't just lobby NVDA heavily to improve nvcc. With the number of GPUs they buy, I suspect they could have anything they wa
Read the rest
nt from the CUDA software teams.
However, if it could compile CUDA for other architectures, sign me up, you'd be my heroes.
For I'd love to see CUDA on Xeon Phi and on AMD GPUs (I know, they're trying). And if Intel poured the same amount of passion and budgeting into building that as they are pouring into fake^H^H^H^Hdeceptive benchmark data and magical powerpoint processors we won't see for at least a year or two (and which IMO will probably disappoint just like the first two), they'd be quite the competitor to NVIDIA, no?
That said, the Intel marketing machine seems to have succeeded in punching NVDA stock in the nose the past few days and in grabbing coverage in Forbes (http://www.forbes.com/sites/aarontilley/2016/08/17/intel-tak...) so maybe they know a thing or two I don't.
srsly? all the discussion above 10hrs+ before your comment and that's your question?
RTFM: https://github.com/amznlabs/amazon-dsstne/blob/master/FAQ.md
In Amazon DSSTNE: Deep Scalable Sparse Tensor Network EngineDeep Learning systems are becoming C++11's halo projects. Here are some deep learning libraries from the Internet Big 4.
Amazon DSSTNE - https://github.com/amznlabs/amazon-dsstne
Google TensorFlow - https://github.com/tensorflow/tensorflow/
Microsoft CNTK - https://github.com/Microsoft/CNTK/
Facebook fbcunn - https://github.com/facebook/fbcunn/
They all utilize C++11 or later. Just as Hadoop pushed Java in the big data, map-reduce realm, I think these libraries will push C++11 in the Deep Learning realm.
In Amazon DSSTNE: Deep Scalable Sparse Tensor Network EngineOne important difference is model-parallel training. From the FAQ:
DSSTNE instead uses “model-parallel training”, where each layer of the network is split across the available GPUs so each operation just runs faster. Model-parallel training is harder to implement, but it doesn’t come with the same speed/accuracy trade-offs of data-parallel training.
https://github.com/amznlabs/amazon-dsstne/blob/master/FAQ.md
In Amazon DSSTNE: Deep Scalable Sparse Tensor Network EngineAmazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
161 points · Open discussion