3 posts tagged with "computation"

Have you ever experienced the frustration of not being able to analyze a dataset because it's too large to fit in memory? Or perhaps you've encountered the memory wall, where computation is hindered by slow memory access? These are common challenges in data science and high-performance computing. The developers of Blosc and Blosc2 have consistently focused on achieving compression and decompression speeds that approach or even exceed memory bandwidth limits.

Moreover, with the introduction of a new compute engine in Blosc2 3.0, the guiding principle has evolved to "Compress Better, Compute Bigger." This enhancement enables computations on datasets that are over 100 times larger than the available RAM, all while maintaining high performance. Continue reading to know how to operate with datasets of 8 TB in human timeframes, using your own hardware.

The Importance of Better Compression​

Data compression typically requires a trade-off between speed and compression ratio. Blosc2 allows users to fine-tune this balance. They can select from a variety of codecs and filters to maximize compression, and even introduce custom ones via its plugin system. For optimal speed, it's crucial to understand and utilize modern CPU capabilities. Multicore processing, SIMD, and cache hierarchies can significantly boost compression performance. Blosc2 leverages these features to achieve speeds close to memory bandwidth limits, and sometimes even surpassing them, particularly with contemporary CPUs.

What is Caterva2?​

Caterva2 is a Free/Open Source distributed system written in Python meant for sharing Blosc2 datasets (either native or converted on-the-fly from HDF5) among different hosts. It uses a publish–subscribe messaging pattern where the data of a publisher can be replicated by an unlimited amount of subscribers. Also, every subscriber exposes a REST interface that allows clients to access the datasets.

Let's suppose that we have a large dataset that we want to share with a group of people. Instead of sending the data to each person individually, we can use Caterva2 to publish the data once and allow multiple subscribers to access it. This way, we can save time and resources by avoiding the need to send the data to each person separately.

Lazy expressions in Caterva2​

Besides the data sharing utility, Caterva2 can also perform operations on the data via Python-Blosc2 v3. These operations range from arithmetic expressions to reductions, filters and broadcasting, and are performed lazily, that is, only when a part of the result is needed.

What expressions are?​

The forthcoming version of Blosc2 will bring a powerful tool for performing mathematical operations on pre-compressed arrays, that is, on arrays whose data has been reduced in size using compression techniques. This functionality provides a flexible and efficient way to perform a wide range of operations, such as addition, subtraction, multiplication and other mathematical functions, directly on compressed arrays. This approach saves time and resources, especially when working with large data sets.

An example of expression computation in Blosc2 might be:

dtype = np.float64
shape = [30_000, 4_000]
size = shape[0] * shape[1]
a = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)
b = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)
c = np.linspace(0, 10, num=size, dtype=dtype).reshape(shape)

#  Convert numpy arrays to Blosc2 arrays
a1 = blosc2.asarray(a, cparams=cparams)
b1 = blosc2.asarray(b, cparams=cparams)
c1 = blosc2.asarray(c, cparams=cparams)

# Perform the mathematical operation
expr = a1 + b1 * c1  # LazyExpr expression
expr += 2  # expressions can be modified
output = expr.compute(cparams=cparams)  # compute! (output is compressed too)

Compressed arrays ( a1, b1, c1) are created from existing numpy arrays ( a, b, c) using Blosc2, then mathematical operations are performed on these compressed arrays using general algebraic expressions. The computation of these expressions is lazy, in that they are not evaluated immediately, but are meant to be evaluated later. Finally, the resulting expression is actually computed (via .compute()) and the desired output (compressed as well) is obtained.

How it works​