1. Clusters of Computational Accelerators
Jan Prins
UNC-Chapel Hill

2. Topics Similarity of accelerator architectures
proof-of-concept kernels for high performance applications
New application areas for accelerator architectures

3. Accelerator architectures
Existing commodity accelerators
Sony/Toshiba/IBM Cell BE
Nvidia G80 GPU
compute-unified device architecture (CUDA)
ATI R600 (almost)
Related developments
Intel demonstrates 80-core TFlop chip
multicore projects
March of progress: next generation GPUs
Roadrunner to be based on 2nd gen Cell

4. Cell BE and Nvidia G80 Cell BE GeForce 8800 GTX
similarities: 8 cores, local store, vectors/Simd (4 vs 16), high speed device memory
differences: Cell integrated PPC, EIB. G80 local caching, extensive multithreading
Cell BE
GeForce 8800 GTX

5. Programming for the memory hierarchy
Local Memory
Global address space
Cache
ALU
Regs
length – latency, thickness – bandwidth, aspect ratio – size of transfers
simple parallel
memory hierarchy (PMH)
simple uniprocessor
memory hierarchy (UMH)

6. Accelerator memory hierarchy
Device Memory
Parallelism
Vector / SIMD
multithreading
multiprocessing
Local Store
Local Store
Vector elts
Vector elts
Vector elts
Vector elts

7. Programming accelerators
Package inherent parallelism available in problem
to provide the concurrency and parallel slack needed at every level of PMH
serialize where needed to reach appropriate level of reuse
Programming models
explicit notion of locality
CUDA
UPC

8. Clusters of Accelerators
Scale
PMH
Peak Perf
Cost
Rack
Global Address Space
20TF
$250K
Node
Local
400GF
$4K
CPU
L2/L3
core L1
Accelerator
Device
200GF
$1K
SIMD
Vector

9. Proof of concept kernels
Demonstrating performance of accelerator clusters
challenge is towards the bottom of the parallel memory hierarchy
proof-of-concept kernels can establish viability and scaling
Example
n-body kernels demonstrated to achieve strong performance on Cell and G80
Consequence
Folding at home clients developed for Playstation and PCs with high-end ATI GPU.
Full GROMACS acceleration on Cell, NAMD acceleration on G80 underway

10. New application domains
Database and datamining operations
Stream mining

11. Stream mining applications
Sampling
Aggregation
Summarization
Clustering
dimensionality reduction
PCA, SVD
subspace clustering
Classification
Anomaly Detection

12. Challenges Continuous data flow Limited storage space
Limited communication bandwidth through hierarchy
Detecting and modeling changes
Visualization

13. Conclusions
Techniques to effectively exploit accelerator clusters are relatively independent of particular choice of accelerator
Application demonstrations can follow spiral development model focusing on implementation of key kernels
Data mining and stream mining are important application areas that may be well served by accelerator architectures