Christian A. Steiner @stoninyc
Zurich Joined March 2011-
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How does it sound having some fun making some compression filters, prefilters, postfilters and codecs, all straight from your favorite language and NumPy? 💖 More info 👇 blosc.org/posts/python-b…… Enjoy Xmas with Python-Blosc2 2.0.0! 🎁
See why #ironArray can make your #data simulation code to fly 🪁🪁: ironarray.io/blog_materials…
🎉🎉New blog on estimating 𝜋 by throwing darts and using ironArray: blog.ironarray.io/estimating-by-… In this installment: high quality random number generators, User Defined Functions and reductions. See how ironArray can consume much less time and memory, compared with #NumPy/#Numba 👇
ironArray implements a matrix multiply that consumes (much) less time & memory than other parallel solutions. Even on Intel machines, ironArray can go at a speed that is just 2x slower than NumPy+MKL. Read more 👉 blog.ironarray.io/matrix-multipl… Save time & energy with ironArray ⚡️🌲
Persistent storage has been gaining performance by leaps and bounds since solid state disks introduction. Check how out-of-core computation can lead to huge savings in memory compared with other solutions, while also performing decently fast: blog.ironarray.io/out-of-core-co…
Did you know that ironArray comes with AI-driven compression algorithms that lets it easily adapt to your preferences? SPEED: favor speed CRATIO: favor compression ratio BALANCE: balance among the two above No more time wasted deciding the codec to use! 👉blog.ironarray.io/surpassing-mem…
Did you know that our array constructors work in parallel and can go more than 10x faster than NumPy? And that by leveraging compression you can host way more data using the same memory resources? More info 👉ironarray.io/docs/html/benc… Enjoy speed and compactness with ironArray!
Did you know that our Enterprise license comes with up to 100 hours of support (negotiable)? We want to *collaborate* with you to make your interaction with ironArray (both the library but *also* the company) as fluid and interactive as possible. Your success is our fulfilment!
Did you know that ironArray can access #Zarr remote arrays in a transparent way via the Zarr proxy array? See our tutorial here: 👇 ironarray.io/docs/html/tuto…
Often times you need to think about a problem from the inside-out. This is how we implemented User Defined Functions to re-define computing for a more cost effective usage of your computing facilities: blog.ironarray.io/inside-out-com…
Surpassing the memory wall requires a wise and well balanced combination of computation and compression, at the right time *and* at the right place. Here is how we are achieving this: medium.com/p/a4351cbc196
In latest 2022.2 release we introduced *parallel constructors* to build arrays using multithreading. This, combined with fine-tuned random generators, allows for amazing speed-ups and memory savings. Check it out in our latest tutorial: ironarray.io/docs/html/tuto…
And the random Poisson (integers) constructor is up to 40x faster, while consuming 10x less memory (with lossless compression):
If you need constructors tailored to your needs, you can easily code them via our powerful User Defined Functions (UDFs). E.g. the UDF below can build a triangular matrix at 3x the speed of NumPy and up to 5x faster than Numba (fully multi-threaded), while using 100x less memory.
Views of views are supported now. This means that you can chain an unlimited number of views on a 'real' array (e.g. a[1:4].astype(np.float64)).
We keep crashing against computer's memory wall. ironArray provides a quick relieve and allows surpassing memory boundaries with easy. Here it is why: blog.ironarray.io/surpassing-mem…
We are not good at predicting the future, but it is a fact that right now SSDs w/ PCIe 4.0 are, by far, a much more cheaper solution than network-based storage. See e.g.: tomshardware.com/reviews/wd-bla… This is why disk-based computing is a real game changer: blog.ironarray.io/out-of-core-co…
As usual, ironArray users already have early access to all of these features before the final release. Hope you would enjoy these features as much as we do implementing them!
Learn why using fine-tuned sizes for the different partitions (chunks & blocks) of ironArray arrays is critical so as to excel in performance and low memory consumption in large matrix multiplications: blog.ironarray.io/matrix-multipl… 👇
After all, and quoting Mahatma Gandhi: "There is more to life than simply increasing its speed"
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