Binary sizes of upgraded versions of software applications tend to be larger, primarily due to feature bloat. This poses various challenges, particularly for mobile applications. It affects upgrade rates directly impacting revenues, increases maintenance costs of supporting multiple versions, and prevents some users from getting critical security fixes. Code bloat also poses a problem for large warehouse-scale applications. Such applications experience performance degradation when their code size exceeds what smaller and more efficient code models can handle.

Analyses of heap-intensive applications show that a small fraction of heap objects account for the majority of heap accesses and data cache misses. Prior works like HDS and HALO have shown that allocating hot objects in separate memory regions can improve spatial locality leading to better application performance. However, these techniques are constrained in two primary ways, limiting their gains. First, these techniques have Imperfect Separation, polluting the hot memory region with several cold objects. Second, reordering of objects across allocations is not possible as the original object allocation order is preserved. This paper presents a novel technique that achieves near perfect separation of hot objects via a new context mechanism that efficiently identifies hot objects with high precision. This technique, named PreFix, is based upon Preallocating memory for a Fixed small number of hot objects. The program, guided by profiles, is instrumented to compute context information derived from dynamic object identifiers, that precisely identifies hot object allocations that are then placed at predetermined locations in the preallocated memory. The preallocated memory region for hot objects provides the flexibility to reorder objects across allocations and allows colocation of objects that are part of a hot data stream (HDS), improving spatial locality. The runtime overhead of identifying hot objects is not significant as this optimization is only focused on a small number of static hot allocation sites and dynamic hot objects. While there is an increase in the program's memory foot-print, it is manageable and can be controlled by limiting the size of the preallocated memory. In addition, PreFix incorporates an object recycling optimization that reuses the same preallocated space to store different objects whose lifetimes are not expected to overlap. Our experiments with 13 heap-intensive applications yield reductions in execution times ranging from 2.77% to 74%. On average PreFix reduces execution time by 21.7% compared to 7.3% by HDS and 14% by HALO. This is due to PreFix's precision in hot object identification, hot object colocation, and low runtime overhead.

While profile guided optimizations (PGO) and link time optimiza-tions (LTO) have been widely adopted, post link optimizations (PLO)have languished until recently when researchers demonstrated that late injection of profiles can yield significant performance improvements. However, the disassembly-driven, monolithic design of post link optimizers face scaling challenges with large binaries andis at odds with distributed build systems. To reconcile and enable post link optimizations within a distributed build environment, we propose Propeller, a relinking optimizer for warehouse scale work-loads. To enable flexible code layout optimizations, we introduce basic block sections, a novel linker abstraction. Propeller uses basic block sections to enable a new approach to PLO without disassembly. Propeller achieves scalability by relinking the binary using precise profiles instead of rewriting the binary. The overhead of relinking is lowered by caching and leveraging distributed compiler actions during code generation. Propeller has been deployed to production at Google with over tens of millions of cores executing Propeller optimized code at any time. An evaluation of internal warehouse-scale applications show Propeller improves performance by 1.1% to 8% beyond PGO and ThinLTO. Compiler tools such as Clang improve by 7% while MySQL improves by 1%. Compared to the state of the art binary optimizer, Propeller achieves comparable performance while lowering memory overheads by 30%-70% on large benchmarks.

Abstract With the advent of multicores, multithreaded programming has acquired increased importance. In order to obtain good performance, the synchronization constructs in multithreaded programs need to be carefully implemented. These implementations can be broadly classified into two categories: busy–wait and schedule‐based. For shared memory architectures, busy–wait synchronizations are preferred over schedule‐based synchronizations because they can achieve lower wakeup latency, especially when the expected wait time is much shorter than the scheduling time. While busy–wait synchronizations can improve the performance of multithreaded programs running on multicore machines, they create a challenge in program debugging, especially in detecting and identifying the causes of data races. Although significant research has been done on data race detection, prior works rely on one important assumption—the debuggers are aware of all the synchronization operations performed during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH‐2 benchmark have busy–wait synchronizations such as barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. To tackle this problem, we propose a dynamic technique for identifying user‐defined synchronizations that are performed during a program run. Both software and hardware implementations are presented. Furthermore, our technique can be easily exploited by a record/replay system to significantly speedup the replay. It can also be leveraged by a transactional memory system to effectively resolve a livelock situation. Our evaluation confirms that our synchronization detector is highly accurate with no false negatives and very few false positives. We further observe that the knowledge of synchronization operations results in 23% reduction in replay time. Finally, we show that using synchronization knowledge livelocks can be efficiently avoided during runtime monitoring of programs. Copyright © 2009 John Wiley & Sons, Ltd.

We present an online framework to capture and recover from program failures and prevent them from occurring in the future through safe execution perturbations. The perturbations are safe as they respect the semantics of the program. We use a checkpointing/logging mechanism to capture a program execution to an event log. If the execution results in a failure, the framework automatically searches for perturbation of the execution by altering the event log and replaying the execution using the altered log to avoid the failure. If found, the perturbation is recorded as a dynamic patch, which is later applied by all future executions of this application to prevent the failure from occurring again. Our experiments show that the proposed framework is very effective in avoiding concurrency faults, heap memory overflow faults, and malicious requests. The entailed overhead for normal execution is very low (2-18%).

Prior work has shown that computing dynamic slices of erroneous program values can greatly assist in locating the root cause of erroneous behavior by identifying faulty statements in sequential programs. These dynamic slices represent backward transitive closure over exercised read-after-write data dependences and control dependences. However, for a multithreaded program executing on a processor, data races represent an additional source of errors which are not captured by dynamic slices. We present an extended form of dynamic slice for multithreaded programs which can assist in locating faults, including those caused by data races. We demonstrate the effectiveness of our approach via case studies and also describe an efficient algorithm for computing dynamic slices.

We are designing scalable dynamic information flow tracking techniques and employing them to carry out tasks related to debugging (bug location and fault avoidance), security (software attack detection), and data validation (lineage tracing of scientific data). The focus of our ongoing work is on developing online dynamic analysis techniques for long running multithreaded programs that may be executed on a single core or on multiple cores to exploit thread level parallelism.

Debugging multithreaded programs, which involves detection and identification of the cause of data races, has proved to be a hard problem. Although there has been significant amount of research on this topic, prior works rely on one important assumption - the debuggers must be aware of all the synchronization operations that take place during a program run. This assumption is a significant limitation as multithreaded programs, including the popular SPLASH-2 benchmark, have barriers and flag synchronizations implemented in the user code. We show that the lack of knowledge of these synchronization operations leads to unnecessary reporting of numerous races. Our experiments with SPLASH-2 benchmark suite show that 12-131 distinct segments in source code, on an average, give rise to well over 4 million dynamic instances of falsely reported races for these programs. We propose a dynamic software technique that identifies the user defined synchronizations exercised during a program run. This information not only helps avoids reporting of unnecessary races, but also helps a record/replay system to speedup the replay.

Debugging long running multithreaded programs is a very challenging problem when using tracing-based analyses. Since such programs are non-deterministic, reproducing the bug is non-trivial and generating and inspecting traces for long running programs can be prohibitively expensive. We propose a framework in which, to overcome the problem of bug reproducibility, a lightweight logging technique is used to log the events during the original execution. When a bug is encountered, it is reproduced using the generated log and during the replay, a fine-grained tracing technique is employed to collect control-flow/dependence traces that are then used to locate the root cause of the bug. In this paper, we address the key challenges resulting due to tracing, that is, the prohibitively high expense of collecting traces and the significant burden on the user who must examine the large amount of trace information to locate the bug in a long-running multithreaded program. These challenges are addressed through execution reduction that realizes a combination of logging and tracing such that traces collected contain only the execution information from those regions of threads that are relevant to the fault. This approach is highly effective because we observe that for long running multithreaded programs, many threads that execute are irrelevant to the fault. Hence, these threads need not be replayed and traced when trying to reproduce the bug. We develop a novel lightweight scheme that identifies such threads by observing all the interthread data dependences and removes their execution footprint in the replay run. In addition, we identify regions of thread executions that need not be replayed or, if they must be replayed, we determine if they need not be traced. Following execution reduction, the replayed execution takes lesser time to run and it produces a much smaller trace than the original execution. Thus, the cost of collecting traces and the effort of examining the traces to locate the fault are greatly reduced.

Execution omission errors are known to be difficult to locate using dynamic analysis. These errors lead to a failure at runtime because of the omission of execution of some statements that would have been executed if the program had no errors. Since dynamic analysis is typically designed to focus on dynamic information arising from executed statements, and statements whose execution is omitted do not produce dynamic information, detection of execution omission errors becomes a challenging task. For example, while dynamic slices are very effective in capturing faulty code for other types of errors, they fail to capture faulty code in presence of execution omission errors. To address this issue relevant slices have been defined to consider certain static dependences (called potential dependences) in addition to dynamic dependences. However, due to the conservative nature of static analysis, overly large slices are produced. In this paper, we propose a fully dynamic solution to locating execution omission errors using dynamic slices. We introduce the notion of implicit dependences which are dependences that are normally invisible to dynamic slicing due to the omission of execution of some statements. We design a dynamic method that forces the execution of the omitted code by switching outcomes of relevant predicates such that those implicit dependences are exposed and become available for dynamic slicing. Dynamic slices can be computed and effectively pruned to produce fault candidate sets containing the execution omission errors. We solve two main problems: verifying the existence of a single implicit dependence through predicate switching, and recovering the implicit dependences in a demand driven manner such that a small number of verifications are required before the root cause is captured. Our experiments show that the proposed technique is highly effective in capturing execution omission errors.

We describe the design, generation, and compression of the extended whole program path (eWPP), representation that not only captures the control flow history of a program execution but also its data dependence history. This representation is motivated by the observation that, typically, a significant fraction of data dependence history can be recovered from the control flow trace. To capture the remainder of the data dependence history, we introduce disambiguation checks in the program whose control flow signatures capture the results of the checks. The resulting extended control flow trace enables the recovery of otherwise irrecoverable data dependences. The code for the checks is designed to minimize the increase in program execution time and the extended control flow trace size when compared to directly collecting control flow and address traces. Our experiments show that compressed eWPPs are only one-quarter of the size of combined compressed control flow and address traces. However, their collection incurs a 5× increase in runtime overhead relative to the overhead required for directly collecting the control flow and address traces, respectively.

Fixing runtime bugs in long running programs using trace based analyses such as dynamic slicing was believed to be prohibitively expensive. In this paper, we present a novel execution fast forwarding technique that makes this feasible. While a naive solution is to divide the entire execution by checkpoints, and then apply dynamic slicing enabled by tracing to one checkpoint interval at a time, it is still too costly even with state-of-the-art tracing techniques. Our technique is derived from two key observations. The first one is that long running programs are usually driven by events, which has been taken advantage of by checkpointing/replaying techniques to deterministically replay an execution from the event log. The second observation is that all the events are not relevant to replaying a particular part of the execution, in which the programmer suspects an error happened. We develop a slicing-like technique that can be used to prune irrelevant events from the event log. Driven by the reduced log, the replayed execution is now traced for fault location. This replayed execution has the effect of fast forwarding, i.e the amount of executed instructions is significantly reduced without losing the accuracy of reproducing a failure. Our evaluation shows that skipping irrelevant events can reduce the space requirement for dynamic slicing by factors ranging from 72 to 44490. We also describe how checkpointing and tracing enabled dynamic slicing are combined, which we believe is the first attempt to integrate these two techniques. Finally, the dynamic slices of a set of reported bugs for long running programs are studied to show the effectiveness of dynamic slicing.

Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible and to ensure that changes to existing software do not break the software. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant as the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. In this paper, we present a new greedy heuristic algorithm for selecting a minimal subset of a test suite T that covers all the requirements covered by T. We show how our algorithm was inspired by the concept analysis framework. We conducted experiments to measure the extent of test suite reduction obtained by our algorithm and prior heuristics for test suite minimization. In our experiments, our algorithm always selected same size or smaller size test suite than that selected by prior heuristics and had comparable time performance.

Since their introduction, path profiles have been used to guide the application of aggressive code optimizations and performing instruction scheduling. However, for optimization and scheduling, it is often desirable to obtain frequency counts of paths that extend across loop iterations and cross procedure boundaries. These longer paths, referred to as interesting paths, account for over 75% of the flow in a subset of SPEC benchmarks. Although the frequency counts of interesting paths can be estimated from path profiles, the degree of imprecision of these estimates is very high. We extend Ball Larus (BL) paths to create slightly longer overlapping paths and develop an instrumentation algorithm to collect their frequencies. While these paths are slightly longer than BL paths, they enable very precise estimation of frequencies of potentially much longer interesting paths. Our experiments show that the average cost of collecting frequencies of overlapping paths is 86.8%, which is 4.2 times that of BL paths. However, while the average imprecision in estimated total flow of interesting paths derived from BL path frequencies ranges from -38 % to +138 %, the average imprecision inflow estimates derived from overlapping path frequencies ranges only from -4% to +8%.

Summary form only given. In embedded devices, like PDAs, limited battery power presents a bottleneck while running power-hungry applications. Hence, it is important for such applications to be energy aware. Consider a scenario where the embedded device is wirelessly linked to a tethered server and can exploit the computing cycles in it. This presents an opportunity to execute parts of an application remotely and reduce computing energy on the embedded device. However, such a scheme is limited by the transmission energy needed in transferring program parts to be executed remotely. We present a fast profile-guided partitioning algorithm that can choose the program parts that can be executed remotely and save energy on the embedded device. We argue that the partitioning might have to be done on the client device more than once and hence the need for a efficient algorithm. We have chosen Java programs in particular because of their widespread use in embedded applications. Experiments show that our approach can conserve energy in embedded devices from 29% to 43%.

Multimedia and network processing applications make extensive use of subword data. Since registers are capable of holding a full data word, when a subword variable is assigned a register, only part of the register is used. New embedded processors have started sup-porting instruction sets that allow direct referencing of bit sections within registers and therefore multiple subword variables can be made to simultaneously reside in the same register without hinder-ing accesses to these variables. However, a new register allocation algorithm is needed that is aware of the bitwidths of program vari-ables and is capable of packing multiple subword variables into a single register. This paper presents one such algorithm. The algorithm we propose has two key steps. First, a combina-tion of forward and backward data flow analyses are developed to determine the bitwidths of program variables throughout the pro-gram. This analysis is required because the declared bitwidths of variables are often larger than their true bitwidths and moreover the minimal bitwidths of a program variable can vary from one pro-gram point to another. Second, a novel interference graph represen-tation is designed to enable support for a fast and highly accurate algorithm for packing of subword variables into a single register. Packing is carried out by a node coalescing phase that precedes the conventional graph coloring phase of register allocation. In contrast to traditional node coalescing, packing coalesces a set of interfering nodes. Our experiments show that our bitwidth aware register allo-cation algorithm reduces the register requirements by 10 % to 50% over a traditional register allocation algorithm that assigns separate registers to simultaneously live subword variables.