  As the use of crowdsourcing increases, it is important to think about
performance optimization. For this purpose, it is possible to think about each
worker as a HPU(Human Processing Unit), and to draw inspiration from
performance optimization on traditional computers or cloud nodes with CPUs.
However, as we characterize HPUs in detail for this purpose, we find that there
are important differences between CPUs and HPUs, leading to the need for
completely new optimization algorithms.
  In this paper, we study the specific optimization problem of obtaining
results fastest for a crowd sourced job with a fixed total budget. In
crowdsourcing, jobs are usually broken down into sets of small tasks, which are
assigned to workers one at a time. We consider three scenarios of increasing
complexity: Identical Round Homogeneous tasks, Multiplex Round Homogeneous
tasks, and Multiple Round Heterogeneous tasks. For each scenario, we analyze
the stochastic behavior of the HPU clock-rate as a function of the remuneration
offered. After that, we develop an optimum Budget Allocation strategy to
minimize the latency for job completion. We validate our results through
extensive simulations and experiments on Amazon Mechanical Turk.
