Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: A computing system includes at least one processor and at least one module operable by the at least one processor to calculate a tail of a first dataset by determining elements of the first dataset that fall outside of a specified percentile, and determine locations of the first dataset at which elements of the first dataset that fall outside of the specified percentile are located. The at least one module may be operable to calculate a tail of a second dataset by populating a data structure with elements of the second dataset that correspond to the locations of the first dataset, and determining, using the data structure, elements of the second dataset that fall outside of the specified percentile. The at least one module may be operable to output an indication of at least one of the tail of the first dataset or the tail of the second dataset.

Abstract: As disclosed herein a method, executed by a computer, for conducting non-recursive cascading reduction includes receiving a collection of floating point values, using a binary representation of an index corresponding to a value being processed to determine a reduction depth for elements on a stack to be accumulated, and according to the reduction depth, iteratively conducting a reduction operation on the current value and one or more values on the stack. In addition to accumulation, the reduction operation may include transforming the value with a corresponding function. The method may also include using a SIMD processing environment to further increase the performance of the method. The method provides results with both high performance and accuracy. A computer system and computer program product corresponding to the method are also disclosed herein.

Abstract: As disclosed herein a method, executed by a computer, for conducting non-recursive cascading reduction includes receiving a collection of floating point values, using a binary representation of an index corresponding to a value being processed to determine a reduction depth for elements on a stack to be accumulated, and according to the reduction depth, iteratively conducting a reduction operation on the current value and one or more values on the stack. In addition to accumulation, the reduction operation may include transforming the value with a corresponding function. The method may also include using a SIMD processing environment to further increase the performance of the method. The method provides results with both high performance and accuracy. A computer system and computer program product corresponding to the method are also disclosed herein.