Abstract-In this paper, we report the results of new experiments that test the performance of Scala parallel collections to find the fair value of riskless bond portfolios using commodity multicore platforms. We developed four algorithms, each of two kinds in Scala and ran them for one to 1024 portfolios, each with a variable number of bonds with daily to yearly cash flows and 1 year to 30 year. We ran each algorithm 11 times at each workload size on three different multicore platforms. We systematically observed the differences and tested them for statistical significance. All the parallel algorithms exhibited super-linear speedup and super-efficiency consistent with maximum performance expectations for scientific computing workloads. The first-order effort or "naïve" parallel algorithms were easiest to write since they followed directly from the serial algorithms. We found we could improve upon the naïve approach with second-order efforts, namely, fine-grain parallel algorithms, which showed the overall best, statistically significant performance, followed by coarse-grain algorithms. To our knowledge these results have not been presented elsewhere.