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Binary floating-point numbers with finite precision cannot represent all decimal numbers with complete accuracy. This can often lead to errors while performing calculations involving floating point numbers. For this reason many commercial applications use special decimal representations for performing these calculations, but their use carries performance costs such as bi-directional conversion. The purpose of this study was to understand the total application performance impact of using these decimal representations in commercial workloads, and provide a foundation of data to justify pursuing optimized hardware support for decimal math. In Java, a popular development environment for commercial applications, the BigDecimal class is used for performing accurate decimal computations. BigDecimal provides operations for arithmetic, scale manipulation, rounding, comparison, hashing, and format conversion. We studied the impact of BigDecimal usage on the performance of server-side Java applications by analyzing its usage on two standard enterprise benchmarks, SPECjbb2005 and SPECjAppServer2004 as well as a real-life mission-critical financial workload, Morgan Stanley's Trade Completion. In this paper, we present detailed performance characteristics and we conclude that, relative to total application performance, the overhead of using software decimal implementations is low, and at least from the point of view of these workloads, there is insufficient performance justification to pursue hardware solutions |
