Although several parallel programming models exist, the Message Passing Interface (MPI) remains the dominant choice for programming supercomputers, with the majority of HPC applications built upon it. A fundamental building block of MPI is the collective communication operations, such as MPI_Bcast or MPI_Allreduce, which are essential communication patterns among a group of processes. Since many scalable applications require scalable MPI collectives, optimizing these collectives is crucial for performance enhancement. However, optimization can only be achieved with precise measurement of the running time of collectives, which is a challenging task in large distributed systems due to issues like unsynchronized distributed clocks. This talk will explore optimizing MPI collectives by accurately measuring their running times, developing novel collective algorithms, and devising methods to tackle the algorithm selection problem when a collective is invoked in an MPI application.