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G-071 committed Mar 11, 2024
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Expand Up @@ -18,7 +18,7 @@ In this use-case, allocating GPU buffers for all sub-grids in advance would have
- Executor pools and various scheduling policies (round robin, priority queue, multi-gpu), which rely on reference counting to gauge the current load of a executor instead of querying the device itself. Tested with CUDA, HIP and Kokkos executors provided by HPX / HPX-Kokkos.
- Special Executors/Allocators for on-the-fly work GPU aggregation (using HPX).

The documentation is available [here](https://sc-sgs.github.io/CPPuddle/index.html). In particular, the public functionality for the memory recycling in available in the namespace [memory_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1memory__recycling.html), for the executor pools it is available in the namespace [executor_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1executor__recycling.html) and the work aggregation (kernel fusion) functionality is available in the namespace [work_aggregation](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1kernel__aggregation.html).
The documentation is available [here](https://sc-sgs.github.io/CPPuddle/). In particular, the public functionality for the memory recycling in available in the namespace [memory_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1memory__recycling.html), for the executor pools it is available in the namespace [executor_recycling](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1executor__recycling.html) and the work aggregation (kernel fusion) functionality is available in the namespace [work_aggregation](https://sc-sgs.github.io/CPPuddle/namespacecppuddle_1_1kernel__aggregation.html).

#### Requirements

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