61,255 research outputs found
Coz: Finding Code that Counts with Causal Profiling
Improving performance is a central concern for software developers. To locate
optimization opportunities, developers rely on software profilers. However,
these profilers only report where programs spent their time: optimizing that
code may have no impact on performance. Past profilers thus both waste
developer time and make it difficult for them to uncover significant
optimization opportunities.
This paper introduces causal profiling. Unlike past profiling approaches,
causal profiling indicates exactly where programmers should focus their
optimization efforts, and quantifies their potential impact. Causal profiling
works by running performance experiments during program execution. Each
experiment calculates the impact of any potential optimization by virtually
speeding up code: inserting pauses that slow down all other code running
concurrently. The key insight is that this slowdown has the same relative
effect as running that line faster, thus "virtually" speeding it up.
We present Coz, a causal profiler, which we evaluate on a range of
highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite.
Coz identifies previously unknown optimization opportunities that are both
significant and targeted. Guided by Coz, we improve the performance of
Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as
much as 68%; in most cases, these optimizations involve modifying under 10
lines of code.Comment: Published at SOSP 2015 (Best Paper Award
Compiler analysis for trace-level speculative multithreaded architectures
Trace-level speculative multithreaded processors exploit trace-level speculation by means of two threads working cooperatively. One thread, called the speculative thread, executes instructions ahead of the other by speculating on the result of several traces. The other thread executes speculated traces and verifies the speculation made by the first thread. In this paper, we propose a static program analysis for identifying candidate traces to be speculated. This approach identifies large regions of code whose live-output values may be successfully predicted. We present several heuristics to determine the best opportunities for dynamic speculation, based on compiler analysis and program profiling information. Simulation results show that the proposed trace recognition techniques achieve on average a speed-up close to 38% for a collection of SPEC2000 benchmarks.Peer ReviewedPostprint (published version
Bio-inspired call-stack reconstruction for performance analysis
The correlation of performance bottlenecks and their associated source code has become a cornerstone of performance analysis. It allows understanding why the efficiency of an application falls behind the computer's peak performance and enabling optimizations on the code ultimately. To this end, performance analysis tools collect the processor call-stack and then combine this information with measurements to allow the analyst comprehend the application behavior. Some tools modify the call-stack during run-time to diminish the collection expense but at the cost of resulting in non-portable solutions. In this paper, we present a novel portable approach to associate performance issues with their source code counterpart. To address it, we capture a reduced segment of the call-stack (up to three levels) and then process the segments using an algorithm inspired by multi-sequence alignment techniques. The results of our approach are easily mapped to detailed performance views, enabling the analyst to unveil the application behavior and its corresponding region of code. To demonstrate the usefulness of our approach, we have applied the algorithm to several first-time seen in-production applications to describe them finely, and optimize them by using tiny modifications based on the analyses.We thankfully acknowledge Mathis Bode for giving us access to the Arts CF binaries, and Miguel Castrillo and Kim Serradell for their valuable insight regarding Nemo. We would like to thank Forschungszentrum JĂĽlich for the computation time on their Blue Gene/Q system. This research has been partially funded by the CICYT under contracts No. TIN2012-34557 and TIN2015-65316-P.Peer ReviewedPostprint (author's final draft
A Fast Causal Profiler for Task Parallel Programs
This paper proposes TASKPROF, a profiler that identifies parallelism
bottlenecks in task parallel programs. It leverages the structure of a task
parallel execution to perform fine-grained attribution of work to various parts
of the program. TASKPROF's use of hardware performance counters to perform
fine-grained measurements minimizes perturbation. TASKPROF's profile execution
runs in parallel using multi-cores. TASKPROF's causal profile enables users to
estimate improvements in parallelism when a region of code is optimized even
when concrete optimizations are not yet known. We have used TASKPROF to isolate
parallelism bottlenecks in twenty three applications that use the Intel
Threading Building Blocks library. We have designed parallelization techniques
in five applications to in- crease parallelism by an order of magnitude using
TASKPROF. Our user study indicates that developers are able to isolate
performance bottlenecks with ease using TASKPROF.Comment: 11 page
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