3 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
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
Online Computation of Critical Paths for Multithreaded Languages
. We have developed an instrumentation scheme that enables programs written in multithreaded languages to compute a critical path at runtime. Our scheme gives not only the length (execution time) of the critical path but also the lengths and locations of all the subpaths making up the critical path. Although the scheme is like Cilk's algorithm in that it uses a "longest path" computation, it allows more flexible synchronization. We implemented our scheme on top of the concurrent object-oriented language Schematic and confirmed its effectiveness through experiments on a 64-processor symmetric multiprocessor. 1 Introduction The scalability expected in parallel programming is often not obtained in the first run, and then performance tuning is necessary. In the early stages of this tuning it is very useful to know what the critical path and how long it is. The length of an execution path is defined as the amount of time needed to execute it, and the critical path is the longest o..