Analysis and Design of an ABFT and Parity-Checking Technique in High Performance Computing Systems

We present a new approach to algorithm-based fault tolerance (ABFT) and parity-checking techniques in the design of high performance computing systems. The ABFT technique employs real convolution error-correcting codes to encode the input data. In order to reduce the round-off error from the output decoding process, systematic real convolution encoding is employed. This paper proposes an efficient method to detect the arithmetic errors using convolution codes at the output compared with an equivalent parity value derived from the input data. Number data processing errors are detected by comparing parity values associated with a convolution code. These comparable sets will be very close numerically, although not identical because of round-off error differences between the two parity generation processes. The effects of internal failures and round-off error are modeled by additive error sources located at the output of the processing block and input at threshold detector. This model combines the aggregate effects of errors and applies them to the respective outputs.

[1]  Karim Mohammadi,et al.  A Generalized ABFT Technique Using a Fault Tolerant Neural Network , 2007, J. Circuits Syst. Comput..

[2]  J. Baylis Error-correcting Codes , 2014 .

[3]  Niraj K. Jha,et al.  Algorithm-based fault tolerance for floating-point operations in massively parallel systems , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[4]  Shuo Wang,et al.  Towards achieving reliable and high-performance nanocomputing via dynamic redundancy allocation , 2009, JETC.

[5]  Jacob A. Abraham,et al.  Algorithm-Based Fault Tolerance for Matrix Operations , 1984, IEEE Transactions on Computers.

[6]  Shu Lin,et al.  Error control coding : fundamentals and applications , 1983 .

[7]  Zizhong Chen,et al.  Algorithm-Based Fault Tolerance for Fail-Stop Failures , 2008, IEEE Transactions on Parallel and Distributed Systems.

[8]  G. Robert Redinbo Wavelet Codes for Algorithm-Based Fault Tolerance Applications , 2010, IEEE Transactions on Dependable and Secure Computing.

[9]  Suku Nair,et al.  Real-Number Codes for Bault-Tolerant Matrix Operations On Processor Arrays , 1990, IEEE Trans. Computers.

[10]  Prithviraj Banerjee,et al.  Algorithms-Based Fault Detection for Signal Processing Applications , 1990, IEEE Trans. Computers.

[11]  J.A. Abraham,et al.  Fault-tolerant matrix arithmetic and signal processing on highly concurrent computing structures , 1986, Proceedings of the IEEE.

[12]  Charles L. H. Lee Convolutional Coding: Fundamentals and Applications , 1997 .

[13]  Abbas Vafaei,et al.  Algorithm Based Fault Tolerant and Check Pointing for High Performance Computing Systems , 2009 .

[14]  Suku Nair,et al.  Algorithm-Based Fault Tolerance on a Hypercube Multiprocessor , 1990, IEEE Trans. Computers.

[15]  G. Robert Redinbo,et al.  Generalized Algorithm-Based Fault Tolerance: Error Correction via Kalman Estimation , 1998, IEEE Trans. Computers.

[16]  R. Morelos-Zaragoza The art of error correcting coding , 2002 .

[17]  Jacob A. Abraham,et al.  Fault-Tolerant FFT Networks , 1988, IEEE Trans. Computers.

[18]  Miroslaw Malek,et al.  A survey of online failure prediction methods , 2010, CSUR.

[19]  Robert H. Morelos-Zaragoza,et al.  The Art of Error Correcting Coding: Morelos-Zaragoza/The Art of Error Correcting Coding, Second Edition , 2006 .

[20]  Elwyn R. Berlekamp,et al.  A Class of Convolution Codes , 1963, Inf. Control..

[21]  G. Robert Redinbo Failure-Detecting Arithmetic Convolutional Codes and an Iterative Correcting Strategy , 2003, IEEE Trans. Computers.

[22]  James L. Massey,et al.  Implementation of burst-correcting convolutional codes , 1965, IEEE Trans. Inf. Theory.

[23]  G. Robert Redinbo Systematic Wavelet Subcodes for Data Protection , 2011, IEEE Transactions on Computers.