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Task-achieving behaviour is designed to make use of the memory when available.
Second, we make a better use of the memory hierarchy, which involves exporting partial summaries to slower storage during the measurement period.
An example is solution methods for initial value problems (IVPs) of ordinary differential equations (ODEs), where the coupling structure of the ODE system to be solved has a large influence on the efficient use of the memory hierarchy of the hardware architecture.
With a better use of the memory types available in the design, an improvement of 61% was achieved in the execution time.
For example, Takouna and Meinel [24] made use of the memory DVFS mechanism for VMs consolidation to cut down the energy consumption.
The compile-time analysis imposes a tight control on the use of the memory hierarchy, hiding the access latency to external memory for many tasks.
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For more complex patterns, the use of the memory-intensive strategy is reduced, while more re-acquisition steps are performed.
This way, though the complexity formula is the same, the family of constants provides the adaptability of the formula to the different stationary uses of the memory.
Therefore, this experiment does not make use of the shared memory to reduce memory bandwidth bottleneck.
In the 3D-DWT, there is an intensive use of memory; therefore, the improvement in the use of the cache memory and data locality justifies efficiencies greater than 1.
PKDGRAV3 has been developed to minimize memory usage per particle (see below) and allow the maximal use of the available memory for particles.
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