Use Parallelized In A Sentence

Word suggestions (2): Parallelism, Parallel

Parallelized

[ˈparələˌlīz]

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Looking for sentences with "Parallelized"? Here are some examples.

1. Use parallelized builds: parallelized builds can reduce total Xcode build times by building components of the app that do not depend on each other at the same time. For projects with many smaller dependencies that can easily be run in parallel, this can offer significant time savings.
2. The new engine has been parallelized and should be substantially faster, and Mozilla believes it can use its new Servo engine to enable capabilities like mixed-reality support far more easily than would've been possible in the old version.
3. Use parallelize() method only when the index of elements does not matter, because once parallelized to partitions, any transformation are done parallelly on partitions. Examples – Spark Parallelize. In the following examples we shall parallelize a Collection of elements to RDD with specified number of partitions.
4. The article makes an in depth analysis of the key aspects that carry significant weight when deciding to parallelize a certain part of an application: the analysis phase of the application that is about to be parallelized; the amount of time involved to achieve the implementation; the feasibility of parallelizing the source code; situations when one should aim for central processing units
5. If all of the code is parallelized, P = 1 and the speedup is infinite (in theory). If 50% of the code can be parallelized, maximum speedup = 2, meaning the code will run twice as fast. Introducing the number of processors performing the parallel fraction of work, the relationship can be modeled by:
6. The compiler detected a store to a scalar variable in the loop body, and that scalar has a use beyond the loop. 1002: The compiler tried to parallelize a loop that has an inner loop that was already parallelized. 1003: The loop body contains an intrinsic call that may read or write to memory. 1004: There is a scalar reduction in the loop body.
7. We can use parallelized apply using the below function. def parallelize_dataframe(df, func, n_cores=4): df_split = _split(df, n_cores) pool = Pool(n_cores) df = ((func, df_split)) () () return df What does it do? It breaks the dataframe into n_cores parts, and spawns n_cores processes which apply the function to all the pieces.
8. For example, a bank can use parallelized batch programs to perform the millions of updates required to apply interest to accounts. The most common example of using parallel execution is for DSS. Complex queries, such as those involving joins or searches of very large tables, are often best run in parallel.
9. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to parallelize MATLAB ® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes.
10. Now that we have the data prepared in the Spark format, we can use MLlib to perform parallelized fitting and model prediction. The snippet below shows how to instantiate and train a linear regression model and calculate the correlation coefficient for the estimated house prices.
11. It seems like code that makes use of the package parallel could be written in a way that the user would have to do minimal code modification to move from using nlm() or optim() to this parallelized optimization routine. That is, it seems one could rewrite these routines basically with no changes, except that the step of calling the model
12. As we learned before, we can use reduce() on parallelized streams. When we use parallelized streams, we should make sure that reduce() or any other aggregate operations executed on the streams are: associative: the result is not affected by the order of the operands; non-interfering: the operation doesn't affect the data source
13. The lize() method is the SparkContext's parallelize method to create a parallelized collection. This allows Spark to distribute the data across multiple nodes, instead of depending on a single node to process the data:
14. Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing - MilesCranmer/PySR. Simple, fast, and parallelized symbolic regression in Python/Julia via regularized evolution and simulated annealing - MilesCranmer/PySR (Don't use the conda-forge version; it doesn't seem to work

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