Single-Threaded Integer Performance: SPEC CPU2006

Even though SPEC CPU2006 is more HPC and workstation oriented, it contains a good variety of integer workloads. Running SPEC CPU2006 is a good way to evaluate single threaded (or core) performance. The main problem is that the results submitted are "overengineered" and it is very hard to make any fair comparisons.

So we wanted to keep the settings as "real world" as possible. We welcome constructive criticism to reach that goal. So we used:

  • 64 bit gcc: most used compiler on Linux, good all round compiler that does not try to "break" benchmarks (libquantum...)
  • -Ofast: compiler optimization that many developers may use
  • -fno-strict-aliasing: necessary to compile some of the subtests
  • base run: every subtest is compiled in the same way.

The ultimate objective is to measure performance in applications where for some reason – as is frequently the case – a "multi thread unfriendly" task keeps us waiting.

Nobody expect the ThunderX to be a single threaded performance wonder. Cavium clearly stated that they deliberately went for a high core count with pretty simple cores. As a result, single threaded performance was not a priority.

However, Facebook and other hyperscalers have indicated that they definitely prefer to get the single threaded performance of a Xeon D. So any competitor challenging Intel should try to keep up with the Xeon D in single threaded performance and offer a throughput-per-dollar/watt bonus. So it is very interesting to measure what single threaded performance the current ThunderX can offer.

Application Type Cavium
2 GHz
Xeon D-1557
Xeon D-1587
Xeon E5-2640 v4
Xeon E5-2690 v3
Xeon E5-2699 v4
Xeon E5-2699 v4
400.perlbench Spam filter 8.3 24.7 29 33.4 39 32.2 36.6
401.bzip2 Compression 6.5 15.1 17.2 19.8 24.2 19.2 25.3
403.gcc Compiling 10.8 23.1 27.2 30 37.2 28.9 33.3
429.mcf Vehicle scheduling 10.2 32.6 38.4 40.4 44.8 39 43.9
445.gobmk Game AI 9.2 17.4 20.2 22.7 28.1 22.4 27.7
456.hmmer Protein seq. analyses 4.8 19 21.7 25.1 28 24.2 28.4
458.sjeng Chess 8.8 19.8 22.8 25.6 31.5 24.8 28.3
462.libquantum Quantum sim 5.8 47.9 58.2 60.3 78 59.2 67.3
464.h264ref Video encoding 11.9 32 36.6 41.9 56 40.7 40.7
471.omnetpp Network sim 7 17.3 23 23.6 30.9 23.5 29.9
473.astar Pathfinding 7.9 14.7 17.2 19.8 24.4 18.9 23.6
483.xalancbmk XML processing 8.4 27.8 33.3 36.2 45.1 35.4 41.8

Although some of you have a mathematical mind and are able to easily decipher these kinds of tables, let the rest of us be lazy and translate this into percentages. We make the Xeon D-1581 the baseline. The Xeon D-1557's performance is more or less the single threaded performance some of the important customers such as Facebook like to have.

Application Type Cavium
2 GHz
Xeon D-1557
Xeon D-1581
Xeon E5-2640
400.perlbench Spam filter 29% 85% 100% 115%
401.bzip2 Compression 38% 88% 100% 115%
403.gcc Compiling 40% 85% 100% 110%
429.mcf Vehicle scheduling 27% 85% 100% 105%
445.gobmk Game AI 46% 86% 100% 112%
456.hmmer Protein seq. analyses 22% 88% 100% 116%
458.sjeng Chess 39% 87% 100% 112%
462.libquantum Quantum sim 10% 82% 100% 104%
464.h264ref Video encoding 33% 87% 100% 114%
471.omnetpp Network sim 30% 75% 100% 103%
473.astar Pathfinding 46% 85% 100% 115%
483.xalancbmk XML processing 25% 83% 100% 109%

First of all, single threaded is somewhat better than we expected when we received the first architectural details (a very simple dual issue core with high latency shared L2). However, this is still a fraction of the Xeon D's single threaded performance, which means that ThunderX doesn't look very impressive to companies which feel that single threaded performance should not be lower than a low end Xeon D. The latter is 2 to 4 times faster. On average, the Xeon D-1581 delivers 3 times faster single threaded performance than the ThunderX, but not 5!

SPEC CPU2006 allows us to characterize the ThunderX core a bit better. We ignore libquantum because it has a very special profile: you can triple the score with specific compiler settings, but those settings reduce performance by 2-30%(!) in some other subtests. Those compiler settings optimize cache utilization by splitting records of an array in separate arrays. Combine this with software loop prefetching and libquantum numbers can indeed double or triple. Since libquantum is hardly relevant for the server world and is known for being a target for all kind of benchmark trickery, we ignore it in our comparison.

Mcf exhibits a large amount of data cache misses and memory controller usage. Mcf is also "horribly low IPC" software, so beefy execution backends do not help. Despite those facts, the ThunderX does not do well in mcf. Mcf does a lot of pointer chasing, so the high latency L2-cache and the high latency DRAM access are slowing things down. That is probably also true for XML processing and the network simulator: those subtests have the highest data cache misses.

The shallow pipeline and relatively powerful gshare branch predictor make the ThunderX a better than expected performer in the chess (sjeng), pathfinding (astar), compiling (gcc) and AI (gobmk). Although the gobmk has a relatively high branch misprediction rate on a gshare branch predictor (the highest of all subtests), the ThunderX core can recover very quickly thanks to its 9 stage pipeline. Notice also that gobmk and gcc have relatively large instruction footprints, which gives the ThunderX and its 78 KB I-cache an advantage.

That is also true for the perl, but that benchmark has a relatively high IPC and needs a beefier execution backend. Indeed, the more compute intensive (and thus high IPC sub tests) perlbench and hmmer perform badly relative to the Intel core. In these benchmarks, the wide architecture of the Intel cores pays off.

Benchmarks Versus Reality Multi-Threaded Integer Performance: SPEC CPU2006


View All Comments

  • vivs26 - Wednesday, June 15, 2016 - link

    Not necessarily - (read Amdahl's law of diminishing returns). The performance actually depends on the workload. Having a million cores guarantees nothing in terms of performance unless the workload is parallelizable which in the real world is not as much as we think it could be. I'm curious to see how xeon merged with altera programmable fabric performs than ARM on a server. Reply
  • maxxbot - Wednesday, June 22, 2016 - link

    Technically true but every generation that millstone gets a little smaller, the die area and power needed to translate x86 into uops isn't huge and reduces every generation. Reply
  • jardows2 - Wednesday, June 15, 2016 - link

    Interesting. Faster in a few workloads where heavy use of multi-thread is important, but significantly slower in more single thread workloads. For server use, you don't always want parallelized tasks. The results are pretty much across the board for all the processors tested: If the ThunderX was slower, it was slower than all the Intel chips. If it were faster, it was faster than all but the highest end Intel Chips. With the price only being slightly lower than the cheapest Intel chip being sold, I don't think this is going to be a Xeon competitor at all, but will take a few niche applications where it can do better.

    With no significant energy savings, we should be looking forward to the ThunderX2 to see if it will bring this into a better alternative.
  • ddriver - Wednesday, June 15, 2016 - link

    There is hardly a server workload where you don't get better throughput by throwing more cores and servers at it. Servers are NOT about parallelized task, but about concurrent tasks. That's why while desktops are still stuck at 8 cores, server chips come with 20 and more... Server workloads are usually very simple, it is just that there is a lot of them. They are so simple and take so little time it literally makes no sense parallelizing them. Reply
  • jardows2 - Wednesday, June 15, 2016 - link

    In the scenario you described, the single-thread performance takes on even more importance, thus highlighting the advantage the Xeon's currently have in most server configurations. Reply
  • niva - Wednesday, June 15, 2016 - link

    Not if the Xeon doesn't have enough cores to actually process 40+ singlethreaded tasks con-currently. Reply
  • hechacker1 - Wednesday, June 15, 2016 - link

    But kernels and VMWare know how to schedule multiple threads on 1 core if it's not being fully utilized. Single threaded IPC can make up for not having as many cores. See the iPhone SoCs for another example. Reply
  • ddriver - Wednesday, June 15, 2016 - link

    Not if you have thousands of concurrent workloads and only like 8 cores. As fast as each core might be, the overhead from workload context switching will eat it up. Reply
  • willis936 - Thursday, June 16, 2016 - link

    Yeah if each task is not significantly longer than a context switch. Context switches are very fast, especially with processors with many sets of SMT registers per core. Reply
  • ddriver - Thursday, June 16, 2016 - link

    If what you suggest is correct, then intel would not be investing chip TDP in more cores but higher clocks and better single threaded performance. Clearly this is not the case, as they are pushing 20 cores at the fairly modest 2.4 Ghz. Reply

Log in

Don't have an account? Sign up now