Turbulent flows carrying a large population of small, inertial particles are commonly observed in nature and engineering applications. The complex interactions between tiny particles and the carrier fluid turbulence observed in such flows are resolved accurately, both in space and time, using Eulerian-Lagrangian direct numerical simulation (DNS). Nevertheless, these computations are resource-intensive, and therefore, parallel computations are necessary to cut down the computation time. This paper presents various parallelization techniques implemented in the in-house code MGLET-LaParT using Message Passing Interface (MPI) to perform Eulerian-Lagrangian DNS of particle-laden turbulent flows. A strong scaling study is performed on Intel Xeon Silver 4116 CPU cluster ‘Pavan’ to assess the performances of implemented parallelization techniques. Relative comparisons of the computation costs of executing critical steps among various parallel algorithms are presented. We perform simulations addressing two different scenarios, uniform and highly non-uniform particle distributions over the MPI subdomains, respectively. We show that the parallelization based on the Eulerian domain decomposition is the computationally most efficient strategy for flow configurations that produce uniform particle distribution over MPI subdomains. On the other hand, the Lagrangian data decomposition based parallelization is an appropriate strategy for flow problems that exhibit highly non-uniform distribution of particles over MPI subdomains.