789 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
GPU in Physics Computation: Case Geant4 Navigation
General purpose computing on graphic processing units (GPU) is a potential
method of speeding up scientific computation with low cost and high energy
efficiency. We experimented with the particle physics simulation toolkit Geant4
used at CERN to benchmark its geometry navigation functionality on a GPU. The
goal was to find out whether Geant4 physics simulations could benefit from GPU
acceleration and how difficult it is to modify Geant4 code to run in a GPU.
We ported selected parts of Geant4 code to C99 & CUDA and implemented a
simple gamma physics simulation utilizing this code to measure efficiency. The
performance of the program was tested by running it on two different platforms:
NVIDIA GeForce 470 GTX GPU and a 12-core AMD CPU system. Our conclusion was
that GPUs can be a competitive alternate for multi-core computers but porting
existing software in an efficient way is challenging
LLOV: A Fast Static Data-Race Checker for OpenMP Programs
In the era of Exascale computing, writing efficient parallel programs is indispensable and at the same time,
writing sound parallel programs is highly difficult. While parallel programming is easier with frameworks
such as OpenMP, the possibility of data races in these programs still persists. In this paper, we propose a
fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM
compiler framework. We compare our tool with other state-of-the-art data race checkers on a variety of
well-established benchmarks. We show that the precision, accuracy, and the F1 score of our tool is comparable
to other checkers while being orders of magnitude faster. To the best of our knowledge, this work is the only
tool among the state-of-the-art data race checkers that can verify a FORTRAN program to be data race free
EOS: A project to investigate the design and construction of real-time distributed embedded operating systems
The EOS project is investigating the design and construction of a family of real-time distributed embedded operating systems for reliable, distributed aerospace applications. Using the real-time programming techniques developed in co-operation with NASA in earlier research, the project staff is building a kernel for a multiple processor networked system. The first six months of the grant included a study of scheduling in an object-oriented system, the design philosophy of the kernel, and the architectural overview of the operating system. In this report, the operating system and kernel concepts are described. An environment for the experiments has been built and several of the key concepts of the system have been prototyped. The kernel and operating system is intended to support future experimental studies in multiprocessing, load-balancing, routing, software fault-tolerance, distributed data base design, and real-time processing
Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters
General purpose computing on graphics processing units (GPGPU) is
dramatically changing the landscape of high performance computing in astronomy.
In this paper, we identify and investigate several key decision areas, with a
goal of simplyfing the early adoption of GPGPU in astronomy. We consider the
merits of OpenCL as an open standard in order to reduce risks associated with
coding in a native, vendor-specific programming environment, and present a GPU
programming philosophy based on using brute force solutions. We assert that
effective use of new GPU-based supercomputing facilities will require a change
in approach from astronomers. This will likely include improved programming
training, an increased need for software development best-practice through the
use of profiling and related optimisation tools, and a greater reliance on
third-party code libraries. As with any new technology, those willing to take
the risks, and make the investment of time and effort to become early adopters
of GPGPU in astronomy, stand to reap great benefits.Comment: 13 pages, 5 figures, accepted for publication in PAS
Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations
We propose Paraiso, a domain specific language embedded in functional
programming language Haskell, for automated tuning of explicit solvers of
partial differential equations (PDEs) on GPUs as well as multicore CPUs. In
Paraiso, one can describe PDE solving algorithms succinctly using tensor
equations notation. Hydrodynamic properties, interpolation methods and other
building blocks are described in abstract, modular, re-usable and combinable
forms, which lets us generate versatile solvers from little set of Paraiso
source codes.
We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A
single source code less than 500 lines can be used to generate solvers of
arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both
manual annotation based tuning and evolutionary computing based automated
tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational
Science and Discover
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