AMD EPYC Milan Review Part 2: Testing 8 to 64 Cores in a Production Platform

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:

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)

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)

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 8280, N=56 for 1S Xeon 8280, and N=128 for 2S and 1S on the EPYC system. The 75F3 system had the worker count reduced to 64 and 32 for 2S/1S runs, with the 7443, 7343 and 72F3 also having the same thread to core ratiio.

The Xeon 8380 was running at N=140 for 2S Xeon 8380 and N=70 for 1S - the benchmark had been erroring out at higher thread counts.

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

EPYC & Altra systems:

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

Xeon Cascade Lake systems:

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

Xeon Ice Lake systems (SNC1):

JAVA_OPTS_C="-server -Xms2g -Xmx2g -Xmn1536m -XX:+UseParallelGC"
JAVA_OPTS_TI="-server -Xms2g -Xmx2g -Xmn1536m -XX:+UseParallelGC"
JAVA_OPTS_BE="-server -Xms192g -Xmx192g -Xmn168g -XX:+UseParallelGC -XX:+AlwaysPreTouch"

Xeon Ice Lake systems (SNC2):

JAVA_OPTS_C="-server -Xms2g -Xmx2g -Xmn1536m -XX:+UseParallelGC"
JAVA_OPTS_TI="-server -Xms2g -Xmx2g -Xmn1536m -XX:+UseParallelGC"
JAVA_OPTS_BE="-server -Xms96g -Xmx96g -Xmn84g -XX:+UseParallelGC -XX:+AlwaysPreTouch"

The reason the Xeon CLX 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.

For the Ice Lake system, I ran both SNC1 (one NUMA node) as SNC2 (two nodes), with the corresponding scaling in the back-end memory allocation.

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.

In the max-jOPS metric, the re-tested 7763 increases its throughput by 5%, while the 75F3 oddly enough didn’t see a notable increase.

The 7343 here doesn’t fare quite as well to the Intel competition as in prior tests, AMD’s high core-to-core latency is still a larger bottleneck in such transactional and database-like workloads compared to Intel’s monolithic mesh approach. Only the 7443 manages to have a slight edge over the 28-core Intel SKU.

In the critical-jOPS metric, both the 16- and 24-core EPYCs lose out to the 28-core Xeon. Unfortunately we don’t have the Xeon 6330 numbers here due those chips and system being in a different location.