Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication links between the base stations and the central computing systems. On the other hand, massive Multiple-Input Multiple-Output (MIMO) technology is a promising solution for base stations toward higher spectral efficiency. To reduce system complexity and energy consumption, 1-bit analog-to-digital converters (ADCs) are leveraged with the cost of lowering the signal quality. To recover the lost information, more sophisticated algorithms, like Joint Channel-and-Data (JCD) estimation, are required whose computation demand is a barrier toward practical implementation. In this paper, we suggest modifications to the current state-of-the-art JCD estimation algorithm to enhance its performance and ease the implementation costs. The modified algorithm delivers up to 4 dB higher symbol error rate in high SNR cases. This improvement is based on a new initialization strategy and an efficient exit condition with lower computation time. We implemented the proposed algorithm on a GPU accelerated system with higher throughput compared to the vanilla BiG-AMP algorithm suitable for software-defined radio systems. To the best of our knowledge, it is the first practical implementation of 1-bit JCD estimation for massive MIMO systems.