SPECjbb MultiJVM - Java Performance

Moving on from SPECCPU, we shift over to SPECjbb2015. SPECjbb is a from ground-up developed benchmark that aims to cover both Java performance and server-like workloads, from the SPEC website:

“The SPECjbb2015 benchmark is based on the usage model of a worldwide supermarket company with an IT infrastructure that handles a mix of point-of-sale requests, online purchases, and data-mining operations. It exercises Java 7 and higher features, using the latest data formats (XML), communication using compression, and secure messaging.

Performance metrics are provided for both pure throughput and critical throughput under service-level agreements (SLAs), with response times ranging from 10 to 100 milliseconds.”

The important thing to note here is that the workload is of a transactional nature that mostly works on the data-plane, between different Java virtual machines, and thus threads.

We’re using the MultiJVM test method where as all the benchmark components, meaning controller, server and client virtual machines are running on the same physical machine.

The JVM runtime we’re using is OpenJDK 15 on both x86 and Arm platforms, although not exactly the same sub-version, but closest we could get:

Altra system:

openjdk 15.0.1 2020-10-20
OpenJDK Runtime Environment 20.9 (build 15.0.1+9)
OpenJDK 64-Bit Server VM 20.9 (build 15.0.1+9, mixed mode, sharing)

EPYC & Xeon systems:

openjdk 15 2020-09-15
OpenJDK Runtime Environment (build 15+36-Ubuntu-1)
OpenJDK 64-Bit Server VM (build 15+36-Ubuntu-1, mixed mode, sharing)

Furthermore, we’re configuring SPECjbb’s runtime settings with the following configurables:

SPEC_OPTS_C="-Dspecjbb.group.count=$GROUP_COUNT -Dspecjbb.txi.pergroup.count=$TI_JVM_COUNT -Dspecjbb.forkjoin.workers=N -Dspecjbb.forkjoin.workers.Tier1=N -Dspecjbb.forkjoin.workers.Tier2=1 -Dspecjbb.forkjoin.workers.Tier3=16"

Where N=160 for 2S Altra test runs, N=80 for 1S Altra test runs, N=112 for 2S Xeon, N=56 for 1S Xeon, and N=128 for 2S and 1S on the EPYC system. I tried running 256 or 160 threads on the 2S EPYC configuration but the benchmark would error out with a critical timeout and I wasn’t able to fully debug as to why it did that.

In terms of JVM options, we’re limiting ourselves to bare-bone options to keep things simple and straightforward:

Altra & EPYC system:

JAVA_OPTS_C="-server -Xms2g -Xmx2g -Xmn1536m"
JAVA_OPTS_TI="-server -Xms2g -Xmx2g -Xmn1536m"
JAVA_OPTS_BE="-server -Xms48g -Xmx48g -Xmn42g -XX:+AlwaysPreTouch"

Xeon system:

JAVA_OPTS_C="-server -Xms2g -Xmx2g -Xmn1536m"
JAVA_OPTS_TI="-server -Xms2g -Xmx2g -Xmn1536m"
JAVA_OPTS_BE="-server -Xms172g -Xmx172g -Xmn156g -XX:+AlwaysPreTouch"

The reason the Xeon system is running a larger back-end heap is because we’re running a single NUMA node per socket, while for the Altra and EPYC we’re running four NUMA nodes per socket for maximised throughput, meaning for the 2S figures we have 8 backends running for the Altra and EPYC and 2 for the Xeon, and naturally half of those numbers for the 1S benchmarks. The back-ends and transaction injectors are affinitised to their local NUMA node with numactl –cpunodebind and –membind, while the controller is called with –interleave=all.

The max-jOPS and critical-jOPS result figures are defined as follows:

"The max-jOPS is the last successful injection rate before the first failing injection rate where the reattempt also fails. For example, if during the RT-curve phase the injection rate of 80000 passes, but the next injection rate of 90000 fails on two successive attempts, then the max-jOPS would be 80000."

"The overall critical-jOPS is computed by taking the geomean of the individual critical-jOPS computed at these five SLA points, namely:

      • Critical-jOPSoverall = Geo-mean of (critical-jOPS@ 10ms, 25ms, 50ms, 75ms and 100ms response time SLAs)

During the RT curve building phase the Transaction Injector measures the 99th percentile response times at each step level for all the requests (see section 9) that are considered in the metrics computations. It then computes the Critical-jOPS for each of the above five SLA points using the following formula:
(first * nOver + last * nUnder) / (nOver + nUnder) "

That’s a lot of technicalities to explain an admittedly complex benchmark, but the gist of it is that max-jOPS represents the maximum transaction throughput of a system until further requests fail, and critical-jOPS is an aggregate geomean transaction throughput within several levels of guaranteed response times, essentially different levels of quality of service.

Beyond the result figures, the benchmark keeps detailed track of timings of responses and tracks a few important statistical data-points across a response-time curve, as follows:

2S EPYC 7742 THP Enabled

I’m starting off with the EPYC results as they’re sort of standard – the max-jOPS here ends up quite high at over 270k, while the critical-jOPS ends up around 125k. The system still manages to retain 90th percentile response times under 20ms up until 230k which is excellent, with 99th percentile results starting to degrade after 110k jOPS.

2S Xeon 8280 THP Enabled

On the Xeon system, we see similar flat 90th percentile response times up until around 120k with 99th percentiles starting to degrade following 90k, but in a much tighter curve than on the EPYC system – while the system here has less overall throughput its scaling up to that throughput limit could be considered to be better.

2S Altra Q80-33 THP Enabled

With the EPYC and Xeon systems as context, we’re finally looking at the Altra results, which look very different.

Unlike the x86 systems, 99th and 90th percentile response times degrade earlier on in the throughput curve for the Altra chip. What this actually reminded me of is the STREAM results from earlier in the review where we saw that initially a bunch of cores were able to hit peak bandwidth across the memory controllers, but adding further cores to the mix actually degraded performance, pointing out to suboptimal congestion across the mesh interconnect.

It might be possible that the results here across SPECjbb are hitting a similar level of saturation under load, given that there’s a lot of inter-core communication and memory transactions happening.

SPECjbb2015-MultiJVM max-jOPS

Charting the max-jOPS of the different systems, I ran figures for both 1S and 2S system configurations. Additionally, I also tested out the benchmark both with transparent huge pages always enabled, and to a default not used / madvise state, as we’ve seen in the past that this can have a notable impact on the resulting performance.

Whilst the Altra system is able to beat the Xeon, it’s not sufficient to match the EPYC system which still lies considerably ahead by a good margin. The exact reasons for this discrepancy compared to the x86 systems isn’t immediately clear, as we’re dealing with many layers here. AArch64 OpenJDK JVM performance certainly might not be as mature and optimised as the x86-64 counterparts, and there is certainly a rabbit hole of various optimisations and knobs we could have tried to change things – although we still view these simple default out-of-the-box settings to still be valuable and valid in terms of comparisons.

One thing that did come to mind immediately when I saw the results was SMT. Due to this being a transactional data-plane resident type of workload, SMT will undoubtedly help a lot in terms of performance, so I tested out the EPYC chip figures with SMT disabled, and indeed max-jOPS went down to 209.5k for the 2S THP enabled results, meaning that SMT accounts for a 29.7% performance benefit in this benchmark.

A further indication that the Altra system is being underutilised on the part of the cores and memory-bottlenecked is its power consumption, which even when fully loaded in the RT curve, it generally hovered around 170-180W per socket, while the x86 systems were filling up their TDPs.

It’s generally these kinds of workloads that SMT works best on, and that’s why IBM can deploy SMT4 or SMT8 processors, and the type of workloads Marvell’s ThunderX was trying to carve a niche or itself with SMT4.

SPECjbb2015-MultiJVM critical-jOPS

For the critical-jOPS figures, the Altra doesn’t do well at all given its response-time curve. Beyond the lack of SMT (The EPYC here again achieves its high score through a 26.4% contribution of the secondary logical cores), we’re maybe looking at a software side immaturity of out-of-the-box Java performance on Arm systems. The figures here shouldn’t be taken with absolute authority with a conclusion that Java performance on the Altra sucks, but at least we’re seeing signs that it doesn’t look too great.

SPEC - Multi-Threaded Performance Compiling LLVM, NAMD Performance


View All Comments

  • realbabilu - Friday, December 18, 2020 - link

    well it support fortran also using Arm Fortran Compiler, unlike m1. Reply
  • realbabilu - Friday, December 18, 2020 - link

    my bad. Numerical Algorithms Group (Nag) has fortran for m1. lets battle begin X86 vs arm Reply
  • GruenSein - Friday, December 18, 2020 - link

    The userbase for fortran on M1 is probably super small anyway. Although.. I can see the HPC cluster entirely made up of Macbook Airs before my eye. Just like the PS3-cluster the air force used to have ;) Reply
  • davidorti - Friday, December 18, 2020 - link

    Wouldn't it be way cheaper a cluster of minis? Reply
  • Flunk - Friday, December 18, 2020 - link

    No, the hardware would be cheaper but the maintenance would be much more time-intensive. That's why companies that need a lot of processing hardware buy enterprise level hardware. The cost of maintaining the system quickly eclipses the hardware costs. And if you're using a computer to make money, quite often the hardware cost is only a small amount of your costs. Reply
  • FunBunny2 - Friday, December 18, 2020 - link

    I dunno about the "quite often the hardware cost is only a small amount of your costs." part. as modern production methods have been ever more automated, (I'm talkin to you, bitcoin mining), there's almost no other cost. now, some may argue, in the extreme case of mining for instance, that power is the largest component; but isn't that 'hardware' cost? it certainly isn't labor or interest or land or even CxOs' cut. fewer and fewer automation efforts are conducted in assembler or even naked C or java or FORTRAN, but in frameworks, often with bespoke syntax and with headcounts way lower than their native languages. so, yeah, now into the foreseeable future, hardware is the biggest byte. Reply
  • at_clucks - Friday, December 18, 2020 - link

    The point was a cluster of Minis would probably be cheaper than a cluster of Airs because why pay for screen, battery, keyboard and all that. Reply
  • Spunjji - Monday, December 21, 2020 - link

    True, but I did enjoy the holistic response. Just think of the potential: batteries are a built-in UPS, and you don't need to mess about with any sort of KVM arrangement - if a node drops out, you can go right to it and poke it to find out what's up! Reply
  • ProDigit - Saturday, December 19, 2020 - link

    I guess the results showing lower TDP despite 100% load, means that the cores are sometimes idling for a part of their clock frequency.
    It means the cpu is lacking buffers, and isn't fully optimized.
  • mode_13h - Sunday, December 20, 2020 - link

    Buffers and even cache can't completely avoid memory bottlenecks.

    Also, you can run a core 100% on code with very little parallelism and not draw much power. Code with lots of ILP and especially vector arithmetic burns a lot more power, which is why AVX2 and especially AVX-512 trigger significant clock-throttling on Intel.

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