Software testing is widely used in industry, but its application in the High Performance Computing area has been scarce. Concolic testing, that automates testing via generation of inputs, has been highly successful for desktop applications and thus recent work on the COMPI tool has extended it to MPI programs. However, COMPI has two limitations. First, it requires the user to specify an upper limit on input size – if the chosen limit is too big, considerable time is wasted and if the chosen limit is too small, the branch coverage achieved is limited. Second, COMPI does not support floating point arithmetic that is common in HPC applications.

Introspective sorting is a ubiquitous sorting algorithm which underlies many large scale distributed systems. Hardware-mediated soft errors can result in comparison and memory errors, and thus cause introsort to generate incorrect output, which in turn disrupts systems built upon introsort; hence, it is critical to incorporate fault tolerance capability within introsort. This paper proposes the first theoretically-sound, practical fault tolerant introsort with negligible overhead: FT-iSort. To tolerate comparison errors, we use minimal TMR protection via exploiting the properties of the effects of soft errors on introsort. This algorithm-based selective protection incurs far less overhead than naïve TMR protection designed to protect an entire application. To tolerate memory errors that escape DRAM error correcting code, we propose XOR-based re-execution. We incorporate our fault tolerance method into the well-known parallel sorting implementation HykSort, and we find that fault tolerant HykSort incurs negligible overhead and obtains nearly the same scalability as unprotected HykSort.

MPI is widely used as the bedrock of HPC applications, but there are no effective systematic software testing techniques for MPI programs. In this paper we develop COMPI, the first practical concolic testing tool for MPI applications. COMPI tackles two major challenges. First, it provides an automated testing tool for MPI programs - it performs concolic execution on a single process and records branch coverage across all. Infusing MPI semantics such as MPI rank and MPI_COMM_WORLD into COMPI enables it to automatically direct testing with various processes' executions as well as automatically determine the total number of processes used in the testing. Second, COMPI employs three techniques to effectively control the cost of testing as too high a cost may prevent its adoption. By capping input values, COMPI is made practical as too large an input can make the testing extremely slow and sometimes even fail as memory needed could exceed the computing platform's memory limit. With two-way instrumentation, we reduce the unnecessary memory and I/O overhead of COMPI and the target program. With constraint set reduction, COMPI keeps significantly fewer constraints by removing redundant ones in the presence of loops so as to avoid redundant tests against these branches. Our evaluation of COMPI uncovered four new bugs in a complex application and achieved 69-86% branch coverage which far exceeds the 1.8-38% coverage achieved via random testing.

While program hangs on large parallel systems can be detected via the widely used timeout mechanism, it is difficult for the users to set the timeout - too small a timeout leads to high false alarm rates and too large a timeout wastes a vast amount of valuable computing resources. To address the above problems with hang detection, this paper presents ParaStack, an extremely lightweight tool to detect hangs in a timely manner with high accuracy, negligible overhead with great scalability, and without requiring the user to select a timeout value. For a detected hang, it provides direction for further analysis by telling users whether the hang is the result of an error in the computation phase or the communication phase. For a computation-error induced hang, our tool pinpoints the faulty process by excluding hundreds and thousands of other processes. We have adapted ParaStack to work with the Torque and Slurm parallel batch schedulers and validated its functionality and performance on Tianhe-2 and Stampede that are respectively the world's current 2nd and 12th fastest supercomputers. Experimental results demonstrate that ParaStack detects hangs in a timely manner at negligible overhead with over 99% accuracy. No false alarm is observed in correct runs taking 66 hours at scale of 256 processes and 39.7 hours at scale of 1024 processes. ParaStack accurately reports the faulty process for computation-error induced hangs.

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.

While many linear algebra libraries have been developed to optimize their performance, no linear algebra library considers their energy efficiency at the library design time. In this paper, we present GreenLA - an energy efficient linear algebra software package that leverages linear algebra algorithmic characteristics to maximize energy savings with negligible overhead. GreenLA is (1) energy efficient: it saves up to several times more energy than the best existing energy saving approaches that do not modify library source codes; (2) high performance: its performance is comparable to the highly optimized linear algebra library MAGMA; and (3) transparent to applications: with the same programming interface, existing MAGMA users do not need to modify their source codes to benefit from GreenLA. Experimental results demonstrate that GreenLA is able to save up to three times more energy than the best existing energy saving approaches while delivering similar performance compared to the state-of-the-art linear algebra library MAGMA.