Predicate Detection for Parallel Computations with Locking Constraints

The happened-before model (or the poset model) has been widely used for modeling the computations (execution traces) of parallel programs and detecting predicates (user-specified conditions). This model captures potential causality as well as locking constraints among the executed events of computations using Lamport\u27s happened-before relation. The detection of a predicate in a computation is performed by checking if the predicate could become true in any reachable global state of the computation. In this paper, we argue that locking constraints are fundamentally different from potential causality. Hence, a poset is not an appropriate model for debugging purposes when the computations contain locking constraints. We present a model called Locking Poset, or a Loset, that generalizes the poset model for locking constraints. Just as a poset captures possibly an exponential number of total orders, a loset captures possibly an exponential number of posets. Therefore, detecting a predicate in a loset is equivalent to detecting the predicate in all corresponding posets. Since determining if a global state is reachable in a computation is a fundamental problem for detecting predicates, this paper first studies the reachability problem in the loset model. We show that the problem is NP-complete. Afterwards, we introduce a subset of reachable global states called lock-free feasible global states such that we can check whether a global state is lock-free feasible in polynomial time. Moreover, we show that lock-free feasible global states can act as "reset" points for reachability and be used to drastically reduce the time for determining the reachability of other global states. We also introduce strongly feasible global states that contain all reachable global states and show that the strong feasibility of a global state can be checked in polynomial time. We show that strong feasibility provides an effective approximation of reachability for many practical applications

Reliable Data Processing Enabled By Program Analysis

Errors pose a serious threat to the output validity of modern data processing, which is often performed by computer programs. In scientific computation, data are collected through instruments or sensors that may be exposed to rough environmental conditions, leading to errors. Furthermore, during the computation process data may not be precisely represented due to the limited precision of the underlying machine, leading to representation errors. Computational processing of these data may hence produce unreliable output results or even faulty conclusions. We call them reliability problems. ^ We consider the reliability problems that are caused by two kinds of errors. The first kind of errors includes input and parameter errors, which originate from the external physical environment. We call these external errors. The other kind of errors is due to the limited representation of floating-point values. They occur when values cannot be precisely represented by machines. We call them internal representation errors, or internal errors. They are usually at a much smaller scale compared to external errors. Nonetheless, such tiny errors may still lead to unreliable results and serious problems. ^ In this dissertation, we develop program analysis techniques to enable reliable data processing. For external errors, we propose techniques to improve the sampling efficiency of Monte Carlo methods, namely execution coalescing and white-box sampling. For internal errors, we develop efficient monitoring techniques to detect instability problems at runtime in floating point program executions