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.

Abstract: Techniques are disclosed for computing a real-time credit risk score. In one example, the method comprises at least one processor generating a computation graph comprising static computation nodes, dynamic computation nodes, and computation edges. The computation graph is a tree. Before receiving the real-time trade, the processor determines a pipeline kernel in the computation graph and computes the respective static information in the pipeline kernel. After computing the static information, the processor receives the real-time trade. The real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. The processor computes, based on the real-time trade and the computed static information, the dynamic information in the pipeline kernel and computes, based on the computed dynamic information, the real-time credit risk score.

Abstract: Techniques are disclosed for computing a real-time credit risk score. In one example, the method comprises at least one processor generating a computation graph comprising static computation nodes, dynamic computation nodes, and computation edges. The computation graph is a tree. Before receiving the real-time trade, the processor determines a pipeline kernel in the computation graph and computes the respective static information in the pipeline kernel. After computing the static information, the processor receives the real-time trade. The real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. The processor computes, based on the real-time trade and the computed static information, the dynamic information in the pipeline kernel and computes, based on the computed dynamic information, the real-time credit risk score.