Spectral calculation and analysis have very important practical applications in astrophysics. The main portion of spectral calculation is to solve a large number of onedimensional numerical integrations at each point of a large three-dimensional parameter space. However, existing widely used solutions still remain in process-level parallelism, which is not competent to tackle numerous compute-intensive small integral tasks. This paper presented a GPU-optimized approach to accelerate the

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... l integration in massive spectral calculation. We also proposed a load balance strategy on hybrid multiple CPUs and GPUs architecture via share memory to maximize performance. The approach was prototyped and tested on the Astrophysical Plasma Emission Code (APEC), a commonly used spectral toolset. Comparing with the original serial version and the 24 CPU cores (2.5GHz) parallel version, our implementation on 3 Tesla C2075 GPUs achieves a speedup of up to 300 and 22 respectively.