The current lack of automatic and speedy labeling of a large number (thousands) of malware samples seen everyday delays the generation of malware signatures and has become a major challenge for anti-virus industries. In this paper, we design, implement and evaluate a novel, scalable framework, called MutantX-S, that can efficiently cluster a large number of samples into families based on programs' static features, i.e., code instruction sequences. MutantX-S is a unique combination of several novel techniques to address the practical challenges of malware clustering. Specifically, it exploits the instruction format of x86 architecture and represents a program as a sequence of opcodes, facilitating the extraction of N-gram features. It also exploits the hashing trick recently developed in the machine learning community to reduce the dimensionality of extracted feature vectors, thus significantly lowering the memory requirement and computation costs. Our comprehensive evaluation on a MutantX-S prototype using a database of more than 130,000 malware samples has shown its ability to correctly cluster over 80% of samples within 2 hours, achieving a good balance between accuracy and scalability. Applying MutantX-S on malware samples created at different times, we also demonstrate that MutantX-S achieves high accuracy in predicting labels for previously unknown malware.