Disciplined approximate programming lets programmers declare which parts of a program can be computed approximately and consequently at a lower energy cost. The compiler proves statically that all approximate computation is properly isolated from precise computation. The hardware is then free to selectively apply approximate storage and approximate computation with no need to perform dynamic correctness checks. In this paper, we propose an efficient mapping of disciplined approximate programming onto hardware. We describe an ISA extension that provides approximate operations and storage, which give the hardware freedom to save energy at the cost of accuracy. We then propose Truffle, a microarchitecture design that efficiently supports the ISA extensions. The basis of our design is dual-voltage operation, with a high voltage for precise operations and a low voltage for approximate operations. The key aspect of the microarchitecture is its dependence on the instruction stream to determine when to use the low voltage. We evaluate the power savings potential of in-order and out-of-order Truffle configurations and explore the resulting quality of service degradation. We evaluate several applications and demonstrate energy savings up to 43%.

Disciplined approximate programming lets programmers declare which parts of a program can be computed approximately and consequently at a lower energy cost. The compiler proves statically that all approximate computation is properly isolated from precise computation. The hardware is then free to selectively apply approximate storage and approximate computation with no need to perform dynamic correctness checks.In this paper, we propose an efficient mapping of disciplined approximate programming onto hardware. We describe an ISA extension that provides approximate operations and storage, which give the hardware freedom to save energy at the cost of accuracy. We then propose Truffle, a microarchitecture design that efficiently supports the ISA extensions. The basis of our design is dual-voltage operation, with a high voltage for precise operations and a low voltage for approximate operations. The key aspect of the microarchitecture is its dependence on the instruction stream to determine when to use the low voltage. We evaluate the power savings potential of in-order and out-of-order Truffle configurations and explore the resulting quality of service degradation. We evaluate several applications and demonstrate energy savings up to 43%.

Concurrency errors in multithreaded programs are difficult to find and fix. We propose Aviso, a system for avoiding schedule-dependent failures. Aviso monitors events during a program's execution and, when a failure occurs, records a history of events from the failing execution. It uses this history to generate schedule constraints that perturb the order of events in the execution and thereby avoids schedules that lead to failures in future program executions. Aviso leverages scenarios where many instances of the same software run, using a statistical model of program behavior and experimentation to determine which constraints most effectively avoid failures. After implementing Aviso, we showed that it decreased failure rates for a variety of important desktop, server, and cloud applications by orders of magnitude, with an average overhead of less than 20% and, in some cases, as low as 5%.

Energy efficiency is a key concern in the design of modern computer systems. One promising approach to energy-efficient computation, approximate computing, trades off output accuracy for significant gains in energy efficiency. However, debugging the actual cause of output quality problems in approximate programs is challenging. This paper presents dynamic techniques to debug and monitor the quality of approximate computations. We propose both offline debugging tools that instrument code to determine the key sources of output degradation and online approaches that monitor the quality of deployed applications. We present two offline debugging techniques and three online monitoring mechanisms. The first offline tool identifies correlations between output quality and the execution of individual approximate operations. The second tracks approximate operations that flow into a particular value. Our online monitoring mechanisms are complementary approaches designed for detecting quality problems in deployed applications, while still maintaining the energy savings from approximation. We present implementations of our techniques and describe their usage with seven applications. Our online monitors control output quality while still maintaining significant energy efficiency gains, and our offline tools provide new insights into the effects of approximation on output quality.

We describe an algorithm to perform symbolic execution of a multithreaded program starting from an arbitrary program context. We argue that this can enable more efficient symbolic exploration of deep code paths in multithreaded programs by allowing the symbolic engine to jump directly to program contexts of interest. The key challenge is modeling the initial context with reasonable precision - an overly approximate model leads to exploration of many infeasible paths during symbolic execution, while a very precise model would be so expensive to compute that computing it would defeat the purpose of jumping directly to the initial context in the first place. We propose a context-specific dataflow analysis that approximates the initial context cheaply, but precisely enough to avoid some common causes of infeasible-path explosion. This model is necessarily approximate - it may leave portions of the memory state unconstrained, leaving our symbolic execution unable to answer simple questions such as "which thread holds lock A?". For such cases, we describe a novel algorithm for evaluating symbolic synchronization during symbolic execution. Our symbolic execution semantics are sound and complete up to the limits of the underlying SMT solver. We describe initial experiments on an implementation in Cloud 9.