  In this paper a novel low-storage method for time-resolved databases is
presented. This approach reduces the storage cost of time-resolved databases by
storing filtered flow fields that target the large and intermediate scales,
while retaining all the information needed to fully reconstruct the flow at the
level of filtered flow fields and complete second-order statistics. This is
done by storing also the filtered turbulent stresses, allowing to recover the
exact effect of the small scales on the large and intermediate scales. A
significant speed-up of the computations is achieved, first, by relaxing the
numerical resolution, which is shown to affect only the dynamics close to the
wall, but not the large scales stored in the database, and, second, by
exploiting the \change{computing power and efficiency} of GPU co-processors
using a new high-resolution hybrid CUDA-MPI code. This speed-up allows running
for physically meaningful times to capture the dynamics of the large scales.
The resulting temporally resolved large-scale database of a turbulent channel
flow up to $Re_\tau=5300$, in large boxes for long times, is briefly
introduced, showing significant indicators of large-scale dynamics with
characteristic times of the order of up to eight eddy turnover times.
