Numerous algorithms for computationally intensive tasks have been developed by researchers that are suitable for execution on hypercube multiprocessors. One characteristic of many of these algorithms is that they are extremely structured and are tuned for the highest performance to execute on hypercube architectures. In this paper, we have looked at parallel algorithm design from a different perspective. In many cases, it may be possible to redesign the parallel algorithms using software techniques so as to provide a low-cost on-line scheme for hardware error detection without any hardware modifications. This approach is called Algorithm-based error detection. In the past, we have applied algorithm-based techniques for on-line error detection on the hypercube and have reported some preliminary results of one specific implementation on some applications. In this paper, we provide an in-depth study of the various issues and tradeoffs available in Algorithm-based error detection, as well as a general methodology for evaluating the schemes. We have illustrated the approach on an extremely useful computation in the field of numerical linear algebra: QR factorization. We have implemented and investigated numerous ways of applying algorithm-based error detection using different system-level encoding strategies for QR factorization. Different schemes have been observed to result in varying error coverages and time overheads. We have reported the results of our studies performed on a 16 processor Intel iPSC-2/D4/MX hypercube multiprocessor. © 1990 IEEE