Investigating Performance of Multi-Threading on Zen 3 and AMD Ryzen 5000

CPU Performance

For simplicity, we are listing the percentage performance differentials in all of our CPU testing – the number shown is the % performance of having SMT2 enabled compared to having the setting disabled. Our benchmark suite consists of over 120 tests, full details of which can be found in our #CPUOverload article.

Here are the single threaded results.

Interestingly enough our single threaded performance was within a single percentage point across the stack (SPEC being +1.2%). Given that ST mode should arguably give more resources to each thread for consistency, the fact that we see no difference means that AMD’s implementation of giving a single thread access to all the resources even in SMT mode is quite good.

The multithreaded tests are a bit more diverse:

Here we have a number of different factors affecting the results.

Starting with the two tests that scored statistically worse with SMT2 enabled: yCruncher and AIBench. Both tests are memory-bound and compute-bound in parts, where the memory bandwidth per thread can become a limiting factor in overall run-time. yCruncher is arguably a math synthetic benchmark, and AIBench is still early-beta AI workloads for Windows, so quite far away from real world use cases.

Most of the rest of the benchmarks are between a +5% to +35% gain, which includes a number of our rendering tests, molecular dynamics, video encoding, compression, and cryptography. This is where we can see both threads on each core interleaving inside the buffers and execution units, which is the goal of an SMT design. There are still some bottlenecks in the system affecting both threads getting absolute full access, which could be buffer size, retire rate, op-queue limitations, memory limitations, etc – each benchmark is likely different.

The two outliers are 3DPM/3DPMavx, and Corona. These three are 45%+, with 3DPM going 66%+. Both of these tests are very light on the cache and memory requirements, and use the increased Zen3 execution port distribution to good use. These benchmarks are compute heavy as well, so splitting some of that memory access and compute in the core helps SMT2 designs mix those operations to a greater effect. The fact that 3DPM in AVX2 mode gets a higher benefit might be down to coalescing operations for an AVX2 load/store implementation – there is less waiting to pull data from the caches, and less contention, which adds to some extra performance.

Overall

In an ideal world, both threads on a core will have full access to all resources, and not block each other. However, that just means that the second thread looks like it has its own core completely. The reverse SMT method, of using one global core and splitting it into virtual cores with no contention, is known as VISC, and the company behind that was purchased by Intel a few years ago, but nothing has come of it yet. For now, we have SMT, and by design it will accelerate some key workloads when enabled.

In our CPU results, the single threaded benchmarks showed no uplift with SMT enabled/disabled in our real-world or synthetic workloads. This means that even in SMT enabled mode, if one thread is running, it gets everything the core has on offer.

For multi-threaded tests, there is clearly a spectrum of workloads that benefit from SMT.

Those that don’t are either hyper-optimized on a one-thread-per-core basis, or memory latency sensitive.

Most real-world workloads see a small uplift, an average of 22%. Rendering and ray tracing can vary depending on the engine, and how much bandwidth/cache/core resources each thread requires, potentially moving the execution bottleneck somewhere else in the chain. For execution limited tests that don’t probe memory or the cache at all, which to be honest are most likely to be hyper-optimized compute workloads, scored up to +77% in our testing.