espeed

https://youtu.be/iqlxsSrd-M4

NB: The slides aren't visible in the video, but here's the corresponding AI Memo by Gerald Jay Sussman and Jack Wisdom from November 2012...

AI Memo 2002-018: The Role of Programming in the Formulation of Ideas (November 2012) [pdf] https://dspace.mit.edu/bitstream/handle/1721.1/6707/AIM-2002...

[1] https://firebase.google.com

SPIRAL: Extreme Performance Portability

  This paper provides an end-to-end discussion of the 
  SPIRAL system, its domain-specific languages, and code 
  generation techniques

[0] https://news.ycombinator.com/item?id=18078230

Docker now working https://www.reddit.com/r/Crostini/comments/9jabhq/docker_now...

This is the primary URL: https://aws.amazon.com/corretto/

* BetrFS: An in-kernel file system that uses Bε trees to organize on-disk storage https://news.ycombinator.com/item?id=18202935

Memory model considerations and storage architecture design gets even more interesting now that NVMe has become a thing. For example, in addition to LMDB, how much more interesting have things become for Redis on NVMe?

* Caching Beyond RAM: The Case for NVMe https://news.ycombinator.com/item?id=17315494

* Intel Optane DC Persistent Memory is officially in Google Cloud https://news.ycombinator.com/item?id=1834816

And there are a few new forward-thinking DB architectures emerging on the scene, some that have been in the works for more than 10 years. Look at the work being done by the Berkeley RISELab team and the architecture behind Fluent DB.

* Ground: A Data Context Service (2017) [pdf] (berkeley.edu), https://news.ycombinator.com/item?id=18415456

What might have been conventional wisdom in the realm of DBs years ago will not be the best practices of today. Architectures have changed too much.

And this is not just true for storage, it's true for compute too. The availability of CPU/GPU/TPU accelerators in the data centers is driving a rethink in compute toward parallel algorithms in the form of Vector/Matrix/Tensor multiplication. The best way to store and index these arrays is something to consider too.

LMDB talk at DEVOXX (2013) [video] https://youtu.be/Rx1-in-a1Xc

LMDB CMU Databaseology Lecture (2015) [video] https://youtu.be/tEa5sAh-kVk

Similarly, see Matei Zaharia's (the original Spark lead) and his team at MIT (he's now at Stanford) SIGMOD 2018 paper called MISTIQUE [4]...

[1] Joe Hellerstein https://github.com/jhellerstein

[2] Berkeley RISELab https://rise.cs.berkeley.edu https://github.com/ucbrise

[3] Fluent Compute Platform https://github.com/fluent-project/fluent

[3] Anna: A Crazy Fast, Super-Scalable, Flexibly Consistent KVS https://databeta.wordpress.com/2018/03/09/anna-kvs/ Discussion: https://news.ycombinator.com/item?id=16551072

[4] MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis https://cs.stanford.edu/~matei/papers/2018/sigmod_mistique.p...

https://en.wikipedia.org/wiki/De_Bruijn_sequence

I don't understand this comment.

Last year, the MIT team showed the GraphBLAS/D4M model can achieve 100M inserts per second on a cluster [1], and it's improved since then.

The GraphBLAS [2] standard has been in the works for more than 10 years. It's the culmination of the initial D4M matrix model design by Jeremy Kepner [3] and his team at MIT Lincoln Laboratory Supercomputing Center, which models graphs in the language of linear algebra.

And for the last ~5 years or so the GraphBLAS software model has been designed in collaboration with hardware teams at Intel, NVIDIA, IBM, and the labs to make chips and architectures optimized for these new matrix models and capable of scaling out to exascale [4].

From a query perspective the GraphBLAS model is even better now that GPU and TPU accelerators are populating the data centers since it means you can now run local graph queries and global graph analytics algorithms on the same system and both return results in less than a second.

* Google Cloud GPUs https://cloud.google.com/gpu/

* Google Cloud TPUs https://cloud.google.com/tpu/

For an overview of GraphBLAS in the context Heterogeneous High-Performance Computing (HHPC) systems running on NVIDIA GPUs and Intel Xeon Phis, see the 2015 talk Scott McMillan [5] gave at the CMU Software Engineering Institute [6].

GraphBLAS is in RedisGraph now [7] -- it uses the official GraphBLAS C implementation written by Tim Davis [8], who as you know implements the underlying sparse matrix algos used in everything from MATLAB to Google Maps -- see his recent talk:

* RedisGraph in the Language of Linear Algebra with GraphBLAS, co-presented by RedisLabs and Tim Davis [video] https://www.youtube.com/watch?v=xnez6tloNSQ

Adoption of the GraphBLAS standard by hardware chip manufacturers is a sign that the golden-age of graphs is upon us -- the the graph hardware/software model has effectively been solved, and the preceding wave created by AI/deep-learning demand led the way for the emergence of GPU/TPU accelerators in the cloud -- all the forces have aligned.

But for whatever reason, I haven't heard anything about Neo4j in terms of GraphBLAS. What's Neo4j's official position on this and the adoption of the GraphBLAS standard?

I realize it's a big change and would require a big architectural overhaul, but it's been in the works for 10 years, and most of the vendors have been involved for the last 5 years.

Has Neo4j been involved in the GraphBLAS design process and/or are you moving toward adopting the standard?

---//---

[1] Achieving 100M database inserts per second using Apache Accumulo and D4M [pdf] http://www.ieee-hpec.org/2014/CD/index_htm_files/FinalPapers...

Previous Discussion: https://news.ycombinator.com/item?id=13465141

[2] GraphBLAS Standard http://graphblas.org

[3] Jeremy Kepner http://www.mit.edu/~kepner/

[4] GraphBLAS: Building Blocks For High Performance Graph Analytics https://crd.lbl.gov/news-and-publications/news/2017/graphbla...

[5] Scott McMillan https://insights.sei.cmu.edu/author/scott-mcmillan/

[6] Graph Algorithms on Future Architectures [video] https://www.youtube.com/watch?v=-sIdS4cz7-4

[7] RedisGraph https://oss.redislabs.com/redisgraph/

[8] Tim Davis http://faculty.cse.tamu.edu/davis/

Previous discussion: https://news.ycombinator.com/item?id=18081978

[1] Redis on Optane https://redislabs.com/blog/redis-enterprise-flash-intel-opta...

[2] GraphBLAS & RedisGraph https://news.ycombinator.com/item?id=18099520

Discussion: Algebra and the Lambda Calculus (1993) https://news.ycombinator.com/item?id=18296384

Is holographic associative memory one of your areas of research too?

Alan Kay: A Personal Computer for Children of All Ages (1972) [pdf] http://www.vpri.org/pdf/hc_pers_comp_for_children.pdf

NB: Double check me on that -- is this the original unpublished one, or the revised one?

Programming and Scaling - Alan Kay https://www.youtube.com/watch?v=YyIQKBzIuBY

There's also one where he discusses anthropology and the human universals, but the title of that talk has escaped me for years. Anyone remember what that one's called?

Deny capabilities for safe, fast actors [pdf]: https://www.doc.ic.ac.uk/~scd/fast-cheap-AGERE.pdf

Sylvan has since joined MSR Cambridge and is working on the distributed runtime. He's giving a talk at QCon London in March, and there are several talks on YouTube where he discusses the ideas he combined into Pony's design [4].

[1] https://github.com/sylvanc

[2] https://github.com/ponylang/ponyc

[3] https://www.ponylang.io/discover/#what-is-pony

[4] https://www.youtube.com/results?search_query=ponylang+sylvan

Connect this idea with Conal Elliott's recently posted paper/talk on "The Simple Essence of Automatic Differentiation": https://news.ycombinator.com/item?id=18306860

Eric Weinstein [1], managing director of Thiel Capital, has been advocating upgrading the current economic calculus with a quantitative model based on gauge theory that can better adapt to changing behavior and changes in consumer preferences across time (for example).

Eric has intimated that James Simons [2] discovered a secret (in the Peter Thiel sense of the word) related to gauge theory during the time Simons and C.N. Yang [3] (as in Yang-Mills Theory [4]) discovered the correspondences in what has become known as the "Wu-Yang Dictionary" [5], which later led to Simons founding Renaissance Technologies [6] in 1982 and is part of the secret-sauce in their mathematical models that has enabled them to dominate quantitative trading ever since. A quote from the 1975 paper:

  ...The mathematics of these results is in fact well
  known to the mathematicians in fiber bundle theory. 
  An identification table of terminologies is given in 
  Sec. V. We should emphasize that our interest in this 
  paper does not lie in the beautiful, deep, and general 
  mathematical development in fiber bundle theory. 
  Rather we are concerned with the necessary concepts to 
  describe the physics of gauge theories. It is 
  remarkable that these concepts have already been 
  intensively studied as mathematical constructs.

[0] Gauge Theory https://en.wikipedia.org/wiki/Gauge_theory

[1] Eric Weinstein https://en.wikipedia.org/wiki/Eric_Weinstein

[2] James Simons https://en.wikipedia.org/wiki/James_Harris_Simons

[3] Nobel Laureate C.N. Yang https://en.wikipedia.org/wiki/Yang_Chen-Ning

[4] Yang-Mills Theory https://en.wikipedia.org/wiki/Yang–Mills_theory

[5] Concept of nonintegrable phase factor and global formulation of gauge fields (AKA the Wu-Yang Dictionary) [pdf] http://www.indiana.edu/~jpac/QCDRef/1970s/Concept%20of%20non...

[6] Renaissance Technologies https://en.wikipedia.org/wiki/Renaissance_Technologies

[7] The conceptual origins of Maxwell's equations and gauge theory, by C.N. Yang (2014) [pdf] http://www.physics.umd.edu/grt/taj/675e/OriginsofMaxwellandG...

Previous Discussion: https://news.ycombinator.com/item?id=16325605

In general, see the Classification of Finite Simple Groups [0]. In terms of specific examples, the minimal classifications of knots [1], trees [2], and braids [3] are 3 instances that popped to mind. From a number-theoretic perspective, the paper Enumerating the Rationals and its derivative works are interesting reads. In terms of spatial structure and spatial decomposition, look at the classification of crystal structures [4], lattices, polytopes (zonotopes are particularly interesting), periodic and aperiodic tilings, the classification of space-filling curves, and the classification of closed surfaces [5]. From a relational perspective, look at the classification of topological spatial relations [6] and Allen's interval algebra [7]. And the one my intuition finds most intriguing is the classification of Group Theory Single Axioms and its relation to my conjecture that division is primary.

[0] Classification of Finite Simple Groups https://en.wikipedia.org/wiki/Classification_of_finite_simpl...

[1] Prime Knots https://en.wikipedia.org/wiki/List_of_prime_knots

[2] Enumerating Trees https://www.cs.virginia.edu/~lat7h/blog/posts/434.html

[3] Braid Group https://en.wikipedia.org/wiki/Braid_group

[4] Crystal Structure https://en.wikipedia.org/wiki/Crystal_structure

[5] Classification of Closed Surfaces https://en.wikipedia.org/wiki/Surface_(topology)#Classificat...

[6] Topological Spatial Relations https://en.wikipedia.org/wiki/Spatial_relation

[7] Allen's Interval Algebra https://en.wikipedia.org/wiki/Allen%27s_interval_algebra

[8] "Yet Another Paper on Group Theory Single Axioms" https://www.cs.unm.edu/~mccune/projects/gtsax/

P.S. Clifford Algebra / Geometric Algebra [00] unifies much of mathematics, e.g. in GA, Maxwell's equations can be reduced to one equation and expressed on one line [01]:

[00] Geometric Algebra https://en.wikipedia.org/wiki/Geometric_algebra

[01] Maxwell's equations formulated in terms of Geometric Algebra https://en.wikipedia.org/wiki/Mathematical_descriptions_of_t...

Founder skill sets should complement each other, but that doesn't mean one needs to be an MBA. Unless it's a case where it's founder who has pondering the problem for years, is the source of the insight ,and they just happen to have an MBA too, investors tend to view MBA founders as a net negative [0] vs strong technical team.

Ideally you want a brilliant generalist who sees the big picture to communicate and lead, someone who can speak both languages (tech and business),Often it's someone with a strong tech background who has grown up in an entrepreneurial family or lived in an entrepreneurial environment long enough to absorb the language and a tacit understanding. Paired with a co-founder who has deep, specialized, world-class expertise and a world-class engineer who loves to code, that's a strong team.

Don't worry about scaling to 10,000 employees before you've even begun. That will be a great problem to have when the time comes, and by then you'll the funds to hire a team of MBAs. You outsource your legal and accounting to specialized firms. When you're ready to raise funds, find a good startup attorney to set up the corp, and they'll who the good accountants are -- ask them to recommend. Until then, focus on your team and the core problem you're trying to solve, understand it from every angle -- from the perspective of multiple domains -- until you see the way it all aligns.

Beyond that, read Paul Graham's essays, absorb the Startup School lectures, and read Zero to One:

* http://www.paulgraham.com/articles.html

* https://www.startupschool.org/

* http://zerotoonebook.com

[0] http://startupclass.samaltman.com/courses/lec03/

Read this book: Spark: The Revolutionary New Science of Exercise and the Brain by John Ratey (http://en.wikipedia.org/wiki/John_Ratey), the professor of psychiatry at Harvard Medical School.

I have posted on the effects of sprints several times over the years so rather than repost again, here's a link to the threads: https://hn.algolia.com/?sort=byPopularity&prefix&page=0&date...

Running sprints were key, but inflammation due to diet was part of it too (a latent issue not widely discussed, but a factor more common than you might think). I solved that by eliminating certain foods one by one that are known to cause inflammation. Peanuts were one, and eliminating aspartame (diet coke) had a noticeable effect within just a few days (I verified it wasn't the caffeine by adding back tea and coffee, and those had no noticeable effect).

Here's some links to the original Harvard and USG research findings on the issues with aspartame and artificial sweeteners/flavors in general, like why it's banned it baby food: https://news.ycombinator.com/item?id=6703802