The document compares the Kepler GPU and Xeon Phi architectures through microbenchmarks that test memory-bound, compute-bound, and latency-bound workloads. It finds that for memory-bound workloads, vectorizing loads and using texture cache improves performance on Kepler, while gathering and aligned loads help Xeon Phi. For compute-bound workloads, vectorizing and using float4/double4 benefits Kepler, and intrinsics aid Xeon Phi. And for latency-bound workloads, loop interchange and skipping the L2 cache helps Kepler, while gathering and aligned loads assist Xeon Phi. The conclusion notes vendor performance data may differ from experiments and examples are available online.

1. Kepler vs Xeon Phi : our measures
and their complete source code
http://www.hpcmagazine.fr/en-couverture/kepler-vs-xeon-phi-nos-mesures/
Florent Duguet, PhD
CEO - Altimesh
http://www.altimesh.com/
... article in French
Presentation & translation by
Ronan Keryell (SILKAN / Aptina)

2. 2 different architectures

3. Some functional analogies...
● Vendor data
● Flops/memop: minimal ratio to avoid waiting for
memory

4. 3 microbenchmarks
From theory to practice...
● 1 memory bound : read a vector
– K20: Naïve/vectorized with float4/use texture cache
– Phi : Naïve/vectorized/gather/aligned vector load
● 1 compute bound : Hörner approximation iterated
(expm1())^12 (= 12 add, 24 mul, 60 madd)
– K20: Naïve/vectorized with float4 or double4
– Phi : Naïve/intrinsics
● 1 latency bound : b[i] += a[i + index[k]]
– K20: Naïve/loop interchange/ __ldg to skip L2$
– Phi : Naïve/vectorized/gather/aligned vector load

5. Memory bound

6. Memory bound

7. Compute bound

8. Compute bound

9. Latency bound

10. Latency bound

11. Conclusion
● (...) = (vendor data)
● Warning : in this experimentation fma counts for
1 FLOP instead of usual (... and constructors !)
2 FLOP
● Disclaimer : examples available :-) on
http://www.hpcmagazine.fr/files/sources/003-Kepler-vs-Phi.zip