Jason Ansel

Kendo: efficient deterministic multithreading in software

Although chip-multiprocessors have become the industry standard, developing parallel applications that target them remains a daunting task. Non-determinism, inherent in threaded applications, causes significant challenges for parallel programmers by hindering their ability to create parallel applications with repeatable results. As a consequence, parallel applications are significantly harder to debug, test, and maintain than sequential programs. This paper introduces Kendo: a new software-only system that provides deterministic multithreading of parallel applications. Kendo enforces a deterministic interleaving of lock acquisitions and specially declared non-protected reads through a novel dynamically load-balanced deterministic scheduling algorithm. The algorithm tracks the progress of each thread using performance counters to construct a deterministic logical time that is used to compute an interleaving of shared data accesses that is both deterministic and provides good load balancing. Kendo can run on todays commodity hardware while incurring only a modest performance cost. Experimental results on the SPLASH-2 applications yield a geometric mean overhead of only 16% when running on 4 processors. This low overhead makes it possible to benefit from Kendo even after an application is deployed. Programmers can start using Kendo today to program parallel applications that are easier to develop, debug, and test.

PetaBricks: a language and compiler for algorithmic choice

It is often impossible to obtain a one-size-fits-all solution for high performance algorithms when considering different choices for data distributions, parallelism, transformations, and blocking. The best solution to these choices is often tightly coupled to different architectures, problem sizes, data, and available system resources. In some cases, completely different algorithms may provide the best performance. Current compiler and programming language techniques are able to change some of these parameters, but today there is no simple way for the programmer to express or the compiler to choose different algorithms to handle different parts of the data. Existing solutions normally can handle only coarse-grained, library level selections or hand coded cutoffs between base cases and recursive cases. We present PetaBricks, a new implicitly parallel language and compiler where having multiple implementations of multiple algorithms to solve a problem is the natural way of programming. We make algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The PetaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. Additionally, we introduce novel techniques to autotune algorithms for different convergence criteria. When choosing between various direct and iterative methods, the PetaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice.

DMTCP: Transparent checkpointing for cluster computations and the desktop

DMTCP (distributed multithreaded checkpointing) is a transparent user-level checkpointing package for distributed applications. Checkpointing and restart is demonstrated for a wide range of over 20 well known applications, including MATLAB, Python, TightVNC, MPICH2, OpenMPI, and runCMS. RunCMS runs as a 680 MB image in memory that includes 540 dynamic libraries, and is used for the CMS experiment of the Large Hadron Collider at CERN. DMTCP transparently checkpoints general cluster computations consisting of many nodes, processes, and threads; as well as typical desktop applications. On 128 distributed cores (32 nodes), checkpoint and restart times are typically 2 seconds, with negligible run-time overhead. Typical checkpoint times are reduced to 0.2 seconds when using forked checkpointing. Experimental results show that checkpoint time remains nearly constant as the number of nodes increases on a medium-size cluster. DMTCP automatically accounts for fork, exec, ssh, mutexes/ semaphores, TCP/IP sockets, UNIX domain sockets, pipes, ptys (pseudo-terminals), terminal modes, ownership of controlling terminals, signal handlers, open file descriptors, shared open file descriptors, I/O (including the readline library), shared memory (via mmap), parent-child process relationships, pid virtualization, and other operating system artifacts. By emphasizing an unprivileged, user-space approach, compatibility is maintained across Linux kernels from 2.6.9 through the current 2.6.28. Since DMTCP is unprivileged and does not require special kernel modules or kernel patches, DMTCP can be incorporated and distributed as a checkpoint-restart module within some larger package.

OpenTuner: an extensible framework for program autotuning

Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building autotuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8χ with little programmer effort.

Language-independent sandboxing of just-in-time compilation and self-modifying code

When dealing with dynamic, untrusted content, such as on the Web, software behavior must be sandboxed, typically through use of a language like JavaScript. However, even for such specially-designed languages, it is difficult to ensure the safety of highly-optimized, dynamic language runtimes which, for efficiency, rely on advanced techniques such as Just-In-Time (JIT) compilation, large libraries of native-code support routines, and intricate mechanisms for multi-threading and garbage collection. Each new runtime provides a new potential attack surface and this security risk raises a barrier to the adoption of new languages for creating untrusted content. Removing this limitation, this paper introduces general mechanisms for safely and efficiently sandboxing software, such as dynamic language runtimes, that make use of advanced, low-level techniques like runtime code modification. Our language-independent sandboxing builds on Software-based Fault Isolation (SFI), a traditionally static technique. We provide a more flexible form of SFI by adding new constraints and mechanisms that allow safety to be guaranteed despite runtime code modifications. We have added our extensions to both the x86-32 and x86-64 variants of a production-quality, SFI-based sandboxing platform; on those two architectures SFI mechanisms face different challenges. We have also ported two representative language platforms to our extended sandbox: the Mono common language runtime and the V8 JavaScript engine. In detailed evaluations, we find that sandboxing slowdown varies between different benchmarks, languages, and hardware platforms. Overheads are generally moderate and they are close to zero for some important benchmark/platform combinations.

Portable performance on heterogeneous architectures

Trends in both consumer and high performance computing are bringing not only more cores, but also increased heterogeneity among the computational resources within a single machine. In many machines, one of the greatest computational resources is now their graphics coprocessors (GPUs), not just their primary CPUs. But GPU programming and memory models differ dramatically from conventional CPUs, and the relative performance characteristics of the different processors vary widely between machines. Different processors within a system often perform best with different algorithms and memory usage patterns, and achieving the best overall performance may require mapping portions of programs across all types of resources in the machine. To address the problem of efficiently programming machines with increasingly heterogeneous computational resources, we propose a programming model in which the best mapping of programs to processors and memories is determined empirically. Programs define choices in how their individual algorithms may work, and the compiler generates further choices in how they can map to CPU and GPU processors and memory systems. These choices are given to an empirical autotuning framework that allows the space of possible implementations to be searched at installation time. The rich choice space allows the autotuner to construct poly-algorithms that combine many different algorithmic techniques, using both the CPU and the GPU, to obtain better performance than any one technique alone. Experimental results show that algorithmic changes, and the varied use of both CPUs and GPUs, are necessary to obtain up to a 16.5x speedup over using a single program configuration for all architectures.

Language and compiler support for auto-tuning variable-accuracy algorithms

Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance optimization in situations where perfect precision is not necessary. To this end, programmers often use approximation algorithms, iterative methods, data resampling, and other heuristics. However, programming such variable accuracy algorithms presents difficult challenges since the optimal algorithms and parameters may change with different accuracy requirements and usage environments. This problem is further compounded when multiple variable accuracy algorithms are nested together due to the complex way that accuracy requirements can propagate across algorithms and because of the size of the set of allowable compositions. As a result, programmers often deal with this issue in an ad-hoc manner that can sometimes violate sound programming practices such as maintaining library abstractions. In this paper, we propose language extensions that expose trade-offs between time and accuracy to the compiler. The compiler performs fully automatic compile-time and installtime autotuning and analyses in order to construct optimized algorithms to achieve any given target accuracy. We present novel compiler techniques and a structured genetic tuning algorithm to search the space of candidate algorithms and accuracies in the presence of recursion and sub-calls to other variable accuracy code. These techniques benefit both the library writer, by providing an easy way to describe and search the parameter and algorithmic choice space, and the library user, by allowing high level specification of accuracy requirements which are then met automatically without the need for the user to understand any algorithm-specific parameters. Additionally, we present a new suite of benchmarks, written in our language, to examine the efficacy of our techniques. Our experimental results show that by relaxing accuracy requirements, we can easily obtain performance improvements ranging from 1.1× to orders of magnitude of speedup.

Autotuning algorithmic choice for input sensitivity

A daunting challenge faced by program performance autotuning is input sensitivity, where the best autotuned configuration may vary with different input sets. This paper presents a novel two-level input learning algorithm to tackle the challenge for an important class of autotuning problems, algorithmic autotuning. The new approach uses a two-level input clustering method to automatically refine input grouping, feature selection, and classifier construction. Its design solves a series of open issues that are particularly essential to algorithmic autotuning, including the enormous optimization space, complex influence by deep input features, high cost in feature extraction, and variable accuracy of algorithmic choices. Experimental results show that the new solution yields up to a 3x speedup over using a single configuration for all inputs, and a 34x speedup over a traditional one-level method for addressing input sensitivity in program optimizations.

Autotuning multigrid with PetaBricks

Algorithmic choice is essential in any problem domain to realizing optimal computational performance. Multigrid is a prime example: not only is it possible to make choices at the highest grid resolution, but a program can switch techniques as the problem is recursively attacked on coarser grid levels to take advantage of algorithms with different scaling behaviors. Additionally, users with different convergence criteria must experiment with parameters to yield a tuned algorithm that meets their accuracy requirements. Even after a tuned algorithm has been found, users often have to start all over when migrating from one machine to another. We present an algorithm and autotuning methodology that address these issues in a near-optimal and efficient manner. The freedom of independently tuning both the algorithm and the number of iterations at each recursion level results in an exponential search space of tuned algorithms that have different accuracies and performances. To search this space efficiently, our autotuner utilizes a novel dynamic programming method to build efficient tuned algorithms from the bottom up. The results are customized multigrid algorithms that invest targeted computational power to yield the accuracy required by the user. The techniques we describe allow the user to automatically generate tuned multigrid cycles of different shapes targeted to the users specific combination of problem, hardware, and accuracy requirements. These cycle shapes dictate the order in which grid coarsening and grid refinement are interleaved with both iterative methods, such as Jacobi or Successive Over-Relaxation, as well as direct methods, which tend to have superior performance for small problem sizes. The need to make choices between all of these methods brings the issue of variable accuracy to the forefront. Not only must the autotuning framework compare different possible multigrid cycle shapes against each other, but it also needs the ability to compare tuned cycles against both direct and (non-multigrid) iterative methods. We address this problem by using an accuracy metric for measuring the effectiveness of tuned cycle shapes and making comparisons over all algorithmic types based on this common yardstick. In our results, we find that the flexibility to trade performance versus accuracy at all levels of recursive computation enables us to achieve excellent performance on a variety of platforms compared to algorithmically static implementations of multigrid. Our implementation uses PetaBricks, an implicitly parallel programming language where algorithmic choices are exposed in the language. The PetaBricks compiler uses these choices to analyze, autotune, and verify the PetaBricks program. These language features, most notably the autotuner, were key in enabling our implementation to be clear, correct, and fast.

Siblingrivalry: online autotuning through local competitions

Modern high performance libraries, such as ATLAS and FFTW, and programming languages, such as PetaBricks, have shown that autotuning computer programs can lead to significant speedups. However, autotuning can be burdensome to the deployment of a program, since the tuning process can take a long time and should be re-run whenever the program, microarchitecture, execution environment, or tool chain changes. Failure to re-autotune programs often leads to widespread use of sub-optimal algorithms. With the growth of cloud computing, where computations can run in environments with unknown load and migrate between different (possibly unknown) microarchitectures, the need for online autotuning has become increasingly important. We present SiblingRivalry, a new model for always-on online autotuning that allows parallel programs to continuously adapt and optimize themselves to their environment. In our system, requests are processed by dividing the available cores in half, and processing two identical requests in parallel on each half. Half of the cores are devoted to a known safe program configuration, while the other half are used for an experimental program configuration chosen by our self-adapting evolutionary algorithm. When the faster configuration completes, its results are returned, and the slower configuration is terminated. Over time, this constant experimentation allows programs to adapt to changing dynamic environments and often outperform the original algorithm that uses the entire system.