Blosc Talk by Francesc Alted from PyData London 2014. Sending data memory to CPU (and back) faster than memcpy()

1. Blosc
Sending data from memory to CPU (and back)
faster than memcpy()
Francesc Alted
Software Architect
PyData London 2014
February 22, 2014

2. About Me
• I am the creator of tools like PyTables,
Blosc, BLZ and maintainer of Numexpr.
• I learnt the hard way that ‘premature
optimization is the root of all evil’.
• Now I only humbly try to optimize if I
really need to and I just hope that Blosc is
not an example of ‘premature optimization’.

3. About Continuum Analytics
• Develop new ways on how data is stored,
computed, and visualized.
• Provide open technologies for data
integration on a massive scale.
• Provide software tools, training, and
integration/consulting services to
corporate, government, and educational
clients worldwide.

4. Overview
• Compressing faster than memcpy(). Really?
• How that can be?
(The ‘Starving CPU’ problem)
• How Blosc works.
• Being faster than memcpy() means that my
programs would actually run faster?

5. Compressing Faster
than memcpy()

6. Interactive Session Starts
• If you
want to experiment with Blosc in
your own machine:
http://www.blosc.org/materials/PyDataLondon-2014.tar.gz
• blosc (blz too for later on) is required (both
are included in conda repository).

7. Open Questions
We have seen that, sometimes, Blosc can actually
be faster than memcpy(). Now:
1. If compression takes way more CPU than
memcpy(), why Blosc can beat it?
2. Does this mean that Blosc can actually
accelerate computations in real
scenarios?

8. “Across the industry, today’s chips are largely
able to execute code faster than we can feed
them with instructions and data.”
!
– Richard Sites, after his article
“It’s The Memory, Stupid!”,
Microprocessor Report, 10(10),1996
The Starving CPU
Problem

9. Memory Access Time
vs CPU Cycle Time

10. Book in
2009

11. The Status of CPU
Starvation in 2014
• Memory latency (~10 ns) is much slower
(between 100x and 250x) than processors.
• Memory bandwidth (~15 GB/s) is
improving at a better rate than memory
latency, but it is also slower than
processors (between 30x and 100x).

12. Blosc Goals and
Implementation

13. Blosc: (de)compressing
faster than memcpy()
Transmission + decompression faster than direct transfer?

14. Taking Advantage of
Memory-CPU Gap
• Blosc is meant to discover redundancy in
data as fast as possible.
• It comes with a series of fast compressors:
BloscLZ, LZ4, Snappy, LZ4HC and Zlib
• Blosc is meant for speed, not for high
compression ratios.

15. Blosc Is All About
Efficiency
• Uses data blocks that fit in L1 or L2 caches
(better speed, less compression ratios).
• Uses multithreading by default.
• The shuffle filter uses SSE2 instructions in
modern Intel and AMD processors.

16. Blocking: Divide and
Conquer

17. Suffling: Improving the
Compression Ratio
The shuffling algorithm does not actually
compress the data; it rather changes the byte
order in the data stream:

18. Shuffling Caveat
• Shuffling usually produces better
compression ratios with numerical data,
except when it does not.
• If you mind about the compression ratio, it
is worth to deactivate it and check (it is
active by default).
• Will see an example on real data later on.

19. Blosc Performance:
Laptop back in 2005

20. Blosc Performance:
Desktop Computer in 2012

21. First Answer for Open
Questions
• Blosc data blocking optimizes the cache
behavior during memory access.
• Additionally, it uses multithreading and
SIMD instructions.
• Add these to the Starved CPU problem and
you have a good hint now on why Blosc can
beat memcpy().

22. How Compression
Works With Real Data?

23. The Need for
Compression
• Compression allows to store more data
using the same storage capacity.
• Sure, it uses more CPU time to compress/
decompress data.
• But, that actually means using more wall
clock time?

24. The Need for a
Compressed Container
• A compressed container is meant to store
data in compressed state and transparently
deliver it uncompressed.
• That means that the user only perceives
that her dataset takes less memory.
• Only less space? What about data access
speed?

25. Example of How Blosc Accelerates Genomics I/O:
SeqDB (backed by Blosc)
Source: Howison, M. High-throughput compression of FASTQ data with SeqDB.
IEEE Transactions on Computational Biology and Bioinformatics.

26. Bloscpack (I)
• Command line interface and serialization
format for Blosc:
!
$ blpk c data.dat
# compress
$ blpk d data.dat.blp
# decompress

27. Bloscpack (II)
• Very convenient for easily serializing your
in-memory NumPy datasets:
>>> a = np.linspace(0, 1, 3e8)
>>> print a.size, a.dtype
300000000 float64
>>> bp.pack_ndarray_file(a, 'a.blp')
>>> b = bp.unpack_ndarray_file('a.blp')
>>> (a == b).all()
True

28. Yet Another Example:
BLZ
• BLZ is a both a format and library that has
been designed as an efficient data container
for Big Data.
• Blosc and Bloscpack are at the heart of it in
order to achieve high-speed compression/
decompression.
• BLZ is one of the backends supported by
our nascent Blaze library.

29. Appending Data in
Large NumPy Objects
array to be enlarged
final array object
Copy!
new data to append
New memory
allocation
• Normally a realloc() syscall will not succeed
• Both memory areas have to exist simultaneously

30. Contiguous vs Chunked
NumPy container
BLZ container
chunk 1
chunk 2
.
.
.
chunk N
Contiguous memory
Discontiguous memory

31. Appending data in BLZ
array to be enlarged
chunk 1
chunk 2
new data to append
X
compress
final array object
chunk 1
chunk 2
new chunk
Only a small amount of data has to be compressed

32. The btable object in BLZ
Chunks
New row to append
• Columns are contiguous in memory
• Chunks follow column order
• Very efficient for querying (specially with a
large number of columns)

33. Second Interactive
Session: BLZ and Blosc
on a Real Dataset

34. Second Hint for Open
Questions
Blosc usage in BLZ means not only less storage
usage (~15x-40x reduction for the real life data
shown), but almost the same access time to
the data (~2x-10x slowdown).
(Still need to address implementation details for
getting better performance)

35. Summary
• Blosc, being able to transfer data faster than
memcpy(), has enormous implications on
data management.
• It is well suited not only for saving memory,
but for allowing close performance to
typical uncompressed data containers.
• It works well not only for synthetic data,
but also for real-life datasets.

36. References
• Blosc: http://www.blosc.org
• Bloscpack: https://github.com/Blosc/bloscpack
• BLZ: http://blz.pydata.org

37. “Across the industry, today’s chips are largely able to execute code
faster than we can feed them with instructions and data. There are no
longer performance bottlenecks in the floating-point multiplier or in
having only a single integer unit. The real design action is in memory
subsystems— caches, buses, bandwidth, and latency.”
!
“Over the coming decade, memory subsystem design will be the only
important design issue for microprocessors.”
!
– Richard Sites, after his article “It’s The Memory, Stupid!”,
Microprocessor Report, 10(10),1996
“Over this decade (2010-2020), memory subsystem optimization
will be (almost) the only important design issue for improving
performance.”
– Me :)

38. Thank you!