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DESCRIPTION

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Package: future
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Version: 1.58.0-9010
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Version: 1.58.0-9011
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Title: Unified Parallel and Distributed Processing in R for Everyone
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Depends:
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R (>= 3.2.0)

NAMESPACE

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importFrom(listenv,mapping)
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importFrom(listenv,parse_env_subset)
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importFrom(parallel,clusterCall)
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importFrom(parallel,clusterEvalQ)
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importFrom(parallel,nextRNGStream)
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importFrom(parallel,nextRNGSubStream)
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importFrom(parallel,stopCluster)

NEWS.md

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## Performance
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* The pre-validation of the cluster worker allotted to a future when
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launched was unnecessarily expensive due to a thinko, e.g. it would
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take ~0.1-0.2 seconds for a multisession future, whereas after the
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fix it is effectly 0.0 seconds.
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launched was unnecessarily expensive due to a thinko since
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**future** 1.40.0 (2025-04-10), e.g. it would take ~0.1-0.2 seconds
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for a multisession future, whereas after the fix it is effectly 0.0
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seconds.
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* Cluster and multisession workers initiates more things when
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created, e.g. pre-loading of packages and memoization of available
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cores. Previously, such steps were performed only when the first
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future was evaluated on a worker.
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* The **future** package is now pre-loaded on the parallel workers as
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soon as a new cluster or multisession future backend is set
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up. Previously, it was loaded with the first future being evaluated
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on a worker.
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* Calling `resolved()` on a lazy `ClusterFuture` would collect the
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result for the first _resolved_ future in order to free up one
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worker slot. Now this is only done if all slots are occupied. The
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net benefit is that lazy cluster futures will be launched faster,
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unless all workers are busy.
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* Cluster and multisession workers initiates more things when
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created, e.g. pre-loading of packages and memoization of available
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cores. Previously, such steps were performed only when the first
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future was evaluated on a worker.
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## Bug Fixes
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* Calls to `resolved()` were not registered by FutureJournal.

R/backend_api-11.ClusterFutureBackend-class.R

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void <- clusterCall(cl = cl, fun = requireNamespace, "RhpcBLASctl", quietly = TRUE)
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## Pre-calculate parallelly::availableCores()
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void <- parallel::clusterEvalQ(cl = cl, parallelly::availableCores())
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void <- clusterEvalQ(cl = cl, parallelly::availableCores())
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cl
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} ## .makeCluster()

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