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Due to high energy prices and reductions in funding, we implement a set of energy saving measures at the IT4Innovations National Supercomputer Center. The measures are selected such as to minimize the performance impact and achieve significant cost, enegy and carbon footprint reduction effect.
The energy saving measures are effective as of 31.1.2023.
Core frequency capping is implemented for the Karolina supercomputer:
|Measure | Value |
|---------------------------------------------------------|---------|
|Compute nodes **cn[001-720]**<br> CPU core frequency capping | 2.100 GHz |
|Accelerated compute nodes **acn[001-72]**<br> CPU core frequency capping | 2.600 GHz |
|Accelerated compute nodes **acn[001-72]**<br> GPU core frequency capping | 1.290 GHz |
The performance impact depends on the [arithmetic intensity][1] of the job.
The [arithmetic intensity][2] is a measure of floating-point operations (FLOPs) performed by a given code (or code section) relative to the amount of memory accesses (Bytes) that are required to support those operations. It is defined as a FLOP per Byte ratio (F/B). It is a characteristics of the particular computational algorithm.
In general, the processor frequency capping has low performance impact for computation with intensity below
the [ridge point][2], such as memory bound computations. For intensive, CPU bound computations, the impact is directly
proportional to the frequency reduction.
On Karolina, relative time increase factor up to 1.3 is observed for intensive workloads on CPU, up to 1.1 on GPU. No slowdown is observed for memory bound workloads.
The enegy efficiency in floating point operations per joule is increased by about 30% for CPU workloads, about 25% for GPU workloads.
## Barbora
None implemented yet.
## NVIDIA DGX-2
None implemented yet.
## Complementary systems
None implemented yet.
[1]: https://en.wikipedia.org/wiki/Roofline_model
[2]: https://people.eecs.berkeley.edu/~kubitron/cs252/handouts/papers/RooflineVyNoYellow.pdf