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Bolt:
Building a distributed ndarray
Jason Wittenbach
Janelia Research Campus (HHMI)
Freeman Lab
t, (x, y, z)
time ~ 104
space ~ 107
~ 1011 elements
~ 1 TB
(x, y, t)
(x, y, z, t)
(x, y, z, c, t)
(n, k)
time
time
time
x
y
neuroscience
astronomy
geospatial
climate science
• a distributed ndarray
• built on PySpark
• conforms to NumPy API
data.mean(axis=0)
data[2, 4:10]
data.T
(t, x, y, z) (x, y, z, t)
(v, w, x, y, z)
(v,w) y
x z
(v,w,x)
(v, w, x, y, z)
y
z
(v, w, x, y, z)
(v)
indexing slicing
apply-along-axis
indexing
transpose
slicing
apply-along-axis
reshape
indexing
transpose
map
slicing
apply-along-axis
reshape
reduce filter
chunking padding
indexing
filter
map
(u,v) x
y
apply-along-axis
reduceByKey
(u,v) x
y
map
transpose
(u,v) x
y
map
transpose
(u,v) x
y
map
transpose
(u,v) x
y
shuffle
(t x,y,z) (x,y,z t)
(t x, y, z)
(t)
x y
z
(t x, y, z)
(t)
x y
z
(t x, y, z)
(t,chunk) (t,chunk)
(t,chunk)
chunking
transpose
+ = shuffle
optimization
???
thanks join us!
Jeremy Freeman
Nicholas Sofroniew
Andrew Osheroff
Freeman Lab
Ken Carlile
Robert Lines
Janelia Scientific
Computing
bolt-project
thunder-project
GitHub
@jsonWittenbach
Twitter

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