… In this example, we start with a single source program consisting of two data parallel …
proceeds asindependent data parallel and task parallel phases. The data parallel phase …

… data parallel algorithms amounted to very regular calculations in simulation and search. Our
current view of the applicability of data parallelism … the amount of data to be operated upon …

… when training data get added or removed. Data parallelism is a straightforward and
popular way to accelerate neural network training. For our purposes, data parallelism refers to …

… data-parallel operations. No aspects of GPUs are exposed to programmers. The library
implementation compiles the data-parallel … tiveness of using data parallelism to program GPUs by …

… evaluation of the PyTorch distributed data parallel module. PyTorch is a … Data parallelism
has emerged as a popular solution for … In general, the technique of distributed data parallelism …

… Unlike task parallelism, data parallelism applies the same operation in … , we can transform
an application with limited data parallelism into one with greater task and data parallelism, …

… for data parallel functional programming, allowing very simple languages, where the view
of aggregate data is particularly abstract. We explain how various data parallel operations can …

… , the code and their interaction signi cantly improves the execution of data parallel programs…
that the realization in the context of data parallel programs is interesting due to challenges …

… of data in parallel can be very hard. MapReduce [6–8] greatly eased this task for dataparallel …
, such as distributing and coordinating the parallel work across many machines, and coping …

… These large data sets are not amenable to study using … programming language, emits data
to an aggregation phase. … exploits the parallelism inherent in having data and computation …