Date: Dec 3, 2013 9:00 - 17:00
Dec 4, 2013 9:00 - 17:00
Location: LRZ Building, University campus Garching, near Munich
This course teaches performance engineering approaches
on the compute node level. "Performance Engineering" as
we define it is more than employing tools to identify
hotspots and bottlenecks. It is about developing a
thorough understanding of the interactions between
software and hardware. This process must start at the
core, socket, and node level, where the code gets
executed that does the actual computational work. Once
the architectural requirements of a code are understood
and correlated with performance measurements, the
potential benefit of optimizations can often be
predicted. We introduce a ?holistic? node-level
performance engineering strategy, apply it to different
algorithms from computational science, and also show how
an awareness of the performance features of an
application may lead to notable reductions in power
consumption.
Introduction
* Intel and AMD x86 architectures
* ccNUMA
* Performance modeling & engineering approaches
* Our Approach
Practical performance analysis
* The LIKWID tools
* Typical performance patterns
Microbenchmarks and the memory hierarchy
* Understanding the memory hierarchy
+ Data transfer between memory levels
+ Write allocate vs. NT stores
+ Modeling of cache hierarchies
+ Contention
* NUMA effects ? anisotropy and asymmetry
Typical node-level software overheads
* Cost of synchronization
Contents: * Work distribution
Example Problem: The 3D Jacobi solver
* Core-level optimizations
+ Blocking
+ Non Temporal stores
+ SIMD vectorization (SSE, AVX)
* Multithreading ? contention at different memory
hierarchies
* Temporal Blocking
Example Problem: The Lattice-Boltzmann Method (LBM)
* Introduction
* Roofline Model
* Data layout
* Non Temporal stores
* Model for in-cache data & multicore scaling
* Sparse representation and options for propagation
Example Problem: Sparse Matrix-Vector Multiplication
* Data layouts
* Performance model ? CPU vs. GPU
* Bandwidth reduction
Example Problem: A backprojection algorithm for CT
reconstruction
* The algorithm
* Naive analysis
* Detailed analysis and performance model
* Optimizations
Energy & Parallel Scalability
* Energy consumption of modern processors
* The energy-to-solution metric
* Performance engineering == power engineering
* Case studies
Between each module, there is time for Questions and
Answers!
Prerequisites Participants must have basic knowledge in programming
with Fortran or C
Language: English
Teachers: Prof. Gerhard Wellen/RRZE, Dr. Georg Hager/RRZE et. al.
LRZ registration form: http://www.lrz-muenchen.de/
Registration: services/schulung/kursanmeldung (Please choose course
HNPF1W13)
Diese Information finden Sie im WWW unter
http://www.lrz-muenchen.de/services/compute/supermuc/aktuell/ali4695/
Matthias Brehm