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The execution pipeline creates a command port and a data port.
Tracking and ensuring the correctness of data is an important part of the execution pipeline.
Performance considerations and additional limitations must be understood and considered in integrating such an execution pipeline in a production environment.
Optimizing pipeline execution Currently, communication occurs over ZeroMQ providing many benefits, including dealing with backlog, automated load-balancing, and the ability to interleave work running different stages of the execution pipeline.
One major benefit of this feature is the ability to scale and test the pipeline at different acquisition rates and therefore be able to provide performance metrics on the behavior of a sequence of algorithms enabling developers to further improve their execution pipeline.
More precisely, and attending to our objectives, we analyze the gains of F l u χ with dataflows for continuous and incremental processing in terms of: i) result convergence, as the dataflow execution pipeline advances; ii) error coverage; and iii) machine loads and resource usage through the amount of executions performed/saved.
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Rather than executing more instructions per second through clock-rate escalation, superscalar processors execute multiple instructions per clock cycle through parallel execution pipelines.
The Diamond 330HiFi DSP core's two superscalar execution pipelines and 64-bit FLIX-format instructions allow the processor to break the 1-instruction-per-clock barrier.
Just as warp engines allow the Starship Enterprise to break the speed-of-light barrier, superscalar execution pipelines allow the Diamond 570T CPU to break the 1-instruction-per-clock barrier that limits conventional scalar RISC architectures.
The Diamond 545CK DSP core's three superscalar execution pipelines, dual 128-bit load/store units, and 64-bit FLIX format instructions allow the processor to break the 1-instruction-perclock barrier.
Other WMS typically assume direct access to component executables and manage the execution of pipelines either locally or on a compute cluster.
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