1. Productive Performance Tools for Heterogeneous Parallel Computing
Allen D. Malony
Department of Computer and Information Science
University of Oregon
Shigeo Fukuda, Lunch with a Helmet On

2. Parallel Performance Engineering and Productivity
Scalable, optimized applications deliver HPC promise
Optimization through performance engineering process
Understand performance complexity and inefficiencies
Tune application to run optimally at scale
How to make the process more effective and productive?
What performance technology should be used?
Performance technology part of larger environment
Programmability, reusability, portability, robustness
Application development and optimization productivity
Process, performance technology, and use will change as parallel systems evolve and grow to extreme scale
Goal is to deliver effective performance with high productivity value now and in the future

3. Heterogeneous Parallel Systems and Performance
What does it mean to be heterogeneous?
Diverse in character or content (New Oxford America)
Not homogeneous (Anonymous)
Diversity in what?
Hardware
processors/cores, memory, interconnection, …
different in computing elements and how they are used
Software
how heterogeneous hardware is programmed
different software models, libraries, frameworks, …
Diversity how? when? space? time?
Variation is operation, interaction, concurrency, ...

4. Why Do We Care? Heterogeneity has been around for a long time
Different programmable components in computer systems
Long history of specialized hardware (e.g., ASPs)
Heterogeneous (computing) technology more accessible
Multicore processors (e.g., 4-core, 6-core, 8-core, ...)
Manycore accelerators (e.g., Tesla, Fermi, Larrabee)
High-performance processing engines (e.g., IBM Cell BE)
Performance is the main driving concern
Heterogeneity is arguably the only path to extreme scale
Heterogeneous software technology required
Greater performance enables more powerful software
Will give rise to more sophisticated software environments

5. Implications for Productive Performance Tools
Current status quo is comfortable
Mostly homogeneous parallel systems and software
Shared-memory multithreading – OpenMP
Distributed-memory message passing – MPI
Parallel computational models are relatively stable (simple)
Corresponding performance models are relatively tractable
Parallel performance tools are just keeping up
Heterogeneity creates richer computational potential
Results in greater performance diversity and complexity
Heterogeneous systems will utilize more sophisticated programming and runtime environments
Performance tools have to support richer computation models and more versatile performance perspectives

6. Heterogeneous Performance Views
Want to create performance views that capture heterogeneous concurrency and execution behavior
Reflect execution logic beyond standard actions
Capture performance semantics at multiple levels
Allow for compatible perspectives that do not conflict
Performance views may have to work with reduced data
Consider “host” path and “external” external paths
Want to capture performance for all execution paths
External execution may be more difficult to measure
What perspective does the host have of external entities?
Determines the semantics of the measurement data
Determines assumptions about entity behavior

7. Task-based Performance View
Consider the “task” abstraction for accelerator scenario
Host regards external execution as a task
Tasks operate concurrently with respect to the host
Requires support for tracking asynchronous execution
Host creates measurement perspective for external task
Maintains local and remote performance data
Tasks may have limited measurement support
May depend on host for performance data I/O
Performance data might be received from external task
How to create a view of external performance
Host
GPU

8. Simple Master-Worker Scenario
Master sends tasks to N workers
Workers report back their performance to master
Done for each piece of work
In general, the external performance data can be arbitrary
single time value
more complete representation of external performance
Build a worker performance perspective in the master
Each work task will appear as a separate “thread”

9. CPU – GPU Execution Scenarios

10. Heterogeneous Complexity Issues
Asynchronous execution (concurrency)
Memory transfer and memory sharing
Interactions between heterogeneous components
Interference between heterogeneous components
Different programming languages/libraries/tools
Availability of performance information
Performance measurement and analysis models
...

11. Trace of LINPACK with GPUs (TAUcuda, Jumpshot)

12. Trace of LINPACK with GPUs (TAUcuda, Vampir)

13. NAMD with CUDA NAMD is a molecular dynamics application (Charm++)
NAMD has been accelerated with CUDA
TAU integrated in Charm++
Apply TAUcuda to NAMD
Four processes with one Tesla GPU for each

14. NAMD with CUDA (4 processes)
GPU kernel

15. Scaling NAMD with CUDA
good GPU performance

16. HMPP Workbench with TAUcuda
Host process
Compute kernel
Transfer kernel