  Stochastic simulations need multiple replications in order to build
confidence intervals for their results. Even if we do not need a large amount
of replications, it is a good practice to speed-up the whole simulation time
using the Multiple Replications In Parallel (MRIP) approach. This approach
usually supposes to have access to a parallel computer such as a symmetric
mul-tiprocessing machine (with many cores), a computing cluster or a computing
grid. In this paper, we propose Warp-Level Parallelism (WLP), a GP-GPU-enabled
solution to compute MRIP on GP-GPUs (General-Purpose Graphics Processing
Units). These devices display a great amount of parallel computational power at
low cost, but are tuned to process efficiently the same operation on several
data, through different threads. Indeed, this paradigm is called Single
Instruction, Multiple Threads (SIMT). Our approach proposes to rely on small
threads groups, called warps, to perform independent computations such as
replications. We have benchmarked WLP with three different models: it allows
MRIP to be computed up to six times faster than with the SIMT computing
paradigm.
