Utility grids such as the Amazon EC2 cloud and Amazon S3 offer computational and storage resources that can be used on-demand for a fee by compute and data-intensive applications. The cost of running an application on such a cloud depends on the compute, storage and communication resources it will provision and consume. Different execution plans of the same application may result in significantly different costs. Using the Amazon cloud fee structure and a real-life astronomy application, we study via simulation the cost performance tradeoffs of different execution and resource provisioning plans. We also study these trade-offs in the context of the storage and communication fees of Amazon S3 when used for long-term application data archival. Our results show that by provisioning the right amount of storage and compute resources, cost can be significantly reduced with no significant impact on application performance.

The cost of doing science on the cloud: the Montage example

Most modern compilers operate by applying a fixed, program-independent sequence of optimizations to all programs. Compiler writers choose a single "compilation sequence", or perhaps a couple of compilation sequences. In choosing a sequence, they may consider performance of benchmarks or other important codes. These sequences are intended as general-purpose tools, accessible through command-line flags such as -O2 and -O3.Specific compilation sequences make a significant difference in the quality of the generated code, whether compiling for speed, for space, or for other metrics. A single universal compilation sequence does not produce the best results over all programs [8, 10, 29, 32]. Finding an optimal program-specific compilation sequence is difficult because the space of potential sequences is huge and the interactions between optimizations are poorly understood. Moreover, there is no systematic exploration of the costs and benefits of searching for good (i.e., within a certain percentage of optimal) program-specific compilation sequences.In this paper, we perform a large experimental study of the space of compilation sequences over a set of known benchmarks, using our prototype adaptive compiler. Our goal is to characterize these spaces and to determine if it is cost-effective to construct custom compilation sequences. We report on five exhaustive enumerations which demonstrate that 80% of the local minima in the space are within 5 to 10% of the optimal solution. We describe three algorithms tailored to search such spaces and report on experiments that use these algorithms to find good compilation sequences. These experiments suggest that properties observed in the enumerations hold for larger search spaces and larger programs. Our findings indicate that for the cost of 200 to 4,550 compilations, we can find custom sequences that are 15 to 25% better than the human-designed fixed-sequence originally used in our compiler.

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Finding effective compilation sequences

Processors with multiple functional units, such as CRAY-1, Cyber 205, and FPS 164, have been used for high-end scientific computation tasks. Much effort has been put into increasing the throughput of such systems. One critical consideration in their design is the identification and implementation of a suitable instruction issuing scheme. Existing approaches do not issue enough instructions per machine cycle to fully utilize the functional units and realize the high-performance level achievable with these powerful execution resources.

An Instruction Issuing Approach to Enhancing Performance in Multiple Functional Unit Processors

Cet article montre comment une optimisation de code objet minutieux a simplifiee un compilateur et la rendu facile a recibler. Les compilateurs croises emettent un code comparable aux compilateurs specifiques vides

/pdf/code-selection-through-object-code-optimization-286u2v7410.pdf

Code selection through object code optimization

Research over the past five years has shown significant performance improvements using a technique called adaptive compilation. An adaptive compiler uses a compile-execute-analyze feedback loop to find the combination of optimizations and parameters that minimizes some performance goal, such as code size or execution time.Despite its ability to improve performance, adaptive compilation has not seen widespread use because of two obstacles: the large amounts of time that such systems have used to perform the many compilations and executions prohibits most users from adopting these systems, and the complexity inherent in a feedback-driven adaptive system has made it difficult to build and hard to use.A significant portion of the adaptive compilation process is devoted to multiple executions of the code being compiled. We have developed a technique called virtual execution to address this problem. Virtual execution runs the program a single time and preserves information that allows us to accurately predict the performance of different optimization sequences without running the code again. Our prototype implementation of this technique significantly reduces the time required by our adaptive compiler.In conjunction with this performance boost, we have developed a graphical-user interface (GUI) that provides a controlled view of the compilation process. By providing appropriate defaults, the interface limits the amount of information that the user must provide to get started. At the same time, it lets the experienced user exert fine-grained control over the parameters that control the system.

/pdf/acme-adaptive-compilation-made-efficient-3hzq94co5c.pdf

ACME: adaptive compilation made efficient

Peephole optimizers improve object code by replacing certain sequences of instructions with better sequences. This paper describes PO, a peephole optimizer that uses a symbolic machine description to simulate pairs of adjacent instructions, replacing them, where possible, with an equivalent single instruction. As a result of this organization, PO is machine independent and can be described formally and concisely: when PO is finished, no instruction, and no pair of adjacent instructions, can be replaced with a cheaper single instruction that has the same effect. This thoroughness allows PO to relieve code generators of much case analysis; for example, they might produce only load/add-register sequences and rely on PO to, where possible, discard them in favor or add-memory, add-immediate, or increment instructions. Experiments indicate that naive code generators can give good code if used with PO.

/pdf/the-design-and-application-of-a-retargetable-peephole-57u7cazzyx.pdf

The Design and Application of a Retargetable Peephole Optimizer

Compilers usually eliminate common subexpressions in intermediate code, not object code. This reduces machine-dependence but misses the machine-dependent common subexpressions introduced by the last phases of code expansion. This paper describes a machine-independent procedure for eliminating machine-specific common subexpressions. It also identifies dead variables, defines windows for a companion peephole optimizer, and forms the basis of a retargetable register allocator. Its techniques for handling machine-specific data should generalize to other optimizations as well.

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Jack W. Davidson

Papers

The Design and Application of a Retargetable Peephole Optimizer

Eliminating redundant object code