We study the problem of scheduling $n$ independent moldable tasks on $m$ processors that arises in large-scale parallel computations. When tasks are monotonic, the best known result is a $(\frac{3}{2}+ε)$-approximation algorithm for makespan minimization with a complexity linear in $n$ and polynomial in $\log{m}$ and $\frac{1}ε$ where $ε$ is arbitrarily small. We propose a new perspective of the existing speedup models: the speedup of a task $T_{j}$ is linear when the number $p$ of assigned processors is small (up to a threshold $δ_{j}$) while it presents monotonicity when $p$ ranges in $[δ_{j}, k_{j}]$; the bound $k_{j}$ indicates an unacceptable overhead when parallelizing on too many processors. The generality of this model is proved to be between the classic monotonic and linear-speedup models. For any given integer $δ\geq 5$, let $u=\left\lceil \sqrt[2]δ \right\rceil-1\geq 2$. In this paper, we propose a $\frac{1}{θ(δ)} (1+ε)$-approximation algorithm for makespan minimization where $θ(δ) = \frac{u+1}{u+2}\left( 1- \frac{k}{m} \right)$ ($m\gg k$). As a by-product, we also propose a $θ(δ)$-approximation algorithm for throughput maximization with a common deadline.