Our implementation includes special-case optimizations for purely real or imaginary z, making its performance competitive with specialized implementations of (e.g). the Dawson function, erfcx, and erfi.
Our implementation includes special-case optimizations for purely real or imaginary "z", making the performance competitive to specialized implementations of the Dawson function, erfcx, and erfi.
Our implementation includes special-case optimizations for purely real or imaginary z, making the performance competitive to specialized implementations of the Dawson function, erfcx, and erfi.
Our implementation includes special-case optimizations for purely real or imaginary z, making the performance competitive with specialized implementations of the Dawson function, erfcx, and erfi.
As a proof of concept, our current DFS-Perf implementation includes several built-in benchmarks and workloads, including machine learning and SQL applications.
Our implementation includes a convenient interface for reading data from Spark RDDs, a Scala interface to the Caffe deep learning framework, and a lightweight multi-dimensional tensor library.
System implementation includes hardware and software.
Our implementation includes this optimization component and the complete formalization has been already presented in [8].
This immediate implementation includes distribution and administration of antivirals, which is a best case situation.
In practical settings, our software implementation includes quick tests of selected data traces to determine appropriate detection thresholds.
Finally, the implementation includes the interaction with multiple pulses in addition to depth averaging in optically dense samples.
