To support parallelizable serverless workflows in applications like media processing, we have prototyped a distributed scheduler called Raptor that reduces both the end-to-end delay time and failure rate of parallelizable serverless workflows. As modern serverless frameworks are typically deployed to extremely large scale distributed computing environments by major cloud providers, Raptor is specifically designed to exploit the property of statistically independent function execution that tends to emerge at very large scales. To demonstrate the effect of horizontal scale on function execution, our evaluation demonstrates that mean delay time improvements provided by Raptor for RSA public-private key pair generation can be accurately predicted by mutually independent exponential random variables, but only once the serverless framework is deployed in a highly available configuration and horizontally scaled across three availability zones.