Prabhat, Jeff Regier, and Keno Fischer present Celeste.jl, a Julia project achieving petascale performance for astronomical data analysis on supercomputers.
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Contents
Welcome! Prabhat's part of the talk
Supercomputers at National Energy Research Supercomputing Center (NERSC)
Top 10 data analytics problems (link to blog post)
Celeste is at top of the list for many reasons
Cori Phase 2, Cray XC-40, the supercomputer that we use
NERSC big data stack
Performance and productivity tradeoff?
Celeste team accomplishment
The Celest.jl collaboration
Project goals. Jeff Regier's part of the talk
Examples of astronomical images that we want to analyze
Challenge of analyzing faint stars and galaxies
Our approach using Bayesian inference
The Celest.jl graphical model
Scientific color priors
The likelihood: p(images|catalog)
Tractable and intractable quantities in Bayesian inference
Problem with integral over too high dimensional space
Variational inference
Julia makes implementation of complicated functions possible
Results for small data
Validation of our methods against well-researched sky region Stripe 82
Numerical optimization scheme and making it parallel
Cori Phase 2 supercomputer, some technical information
Performance results of Celest.jl
Solving large-scale I/O problem. Keno Fischer part of the talk
Problem that we encounter with I/O
We take down the network with data traffic that we created
Standard high-performance guidelines Julia code
Majority of hot code in Celest.jl compute Hessian matrix of the objective function
Analyzing for loops in the code
Question: what is ILP (Instruction Level Parallelism)
How much ILP is required?
Putting a large chunk of Julia and assemble code in one big scalar inner loop and vectoring it
Improving Julia and LLVM to allow the aforementioned vectorization
Improvements from the previous point are slowly moving into Julia proper
Using StaticArrays.jl
Optimization of memory layout to avoid vector shuffling
Using multiple dispatch to utilize Hessian matrix structure
Optimization takeaways
Conclusions
Closing information
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Why it's here
3 people recommend this
What people say. Recommended as an impressive demonstration of Julia's capabilities in high-performance scientific computing.
Moments people linked to
N
u/Nuauain r/programming • Recommends
On “Julia 1.0”
Julia is very good with memory allocation and data layout. Julia structs are just memory footprints; you can take a pointer to a C object and reinterpret the memory with a julia struct.
And since its quite generic you can do more abstract/general optimization, for example in the Celeste project (the petaflop thing) they had custom indexing for matrices that took advantage of the structure of the matrix so if fits nicely into memory (the code was the same Ai,j but the way of accessing i,j was different depending on the matrix structure).
Edit: found the video link to this video
On “Julia - the language trying to replace Python in scientific computing”
…e. Compare numpy's atanh implementation with Julia's one. Applying it inplace (no memory allocations) on your custom complex sparse matrix type is as easy as x .= atanh.(x), because everything is written in Julia and is generic.
And Julia was already used for things that are impossible in other scientific programming languages (besides Fortran):
link to this video
This part of the talk is also quite nice to show the advantage of using a language with high-level abstraction for HPC:
link to this video…
