From 4f11a7c57a5481031e507b72b81111dc3a6702e7 Mon Sep 17 00:00:00 2001 From: Branislav Jansik <branislav.jansik@vsb.cz> Date: Tue, 31 Jan 2023 17:15:54 +0100 Subject: [PATCH] Update energy.md --- docs.it4i/general/energy.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs.it4i/general/energy.md b/docs.it4i/general/energy.md index a45dfb658..6ec5d96db 100644 --- a/docs.it4i/general/energy.md +++ b/docs.it4i/general/energy.md @@ -21,15 +21,13 @@ Core frequency capping is implemented for the Karolina supercomputer: 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. +In general, the processor frequency capping has low performance impact for memory bound computations with intensity below the [ridge point][2]. For CPU bound computations with intensity above the [ridge point][2], 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. +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. ### Energy Saved -The enegy efficiency in floating point operations per joule is increased by about 30% for CPU workloads, about 25% for GPU workloads. The efficiency depends on the arithmetic intensity, however energy savings are always achieved. +The enegy efficiency in floating point operations per energy unit is increased by about 30% for CPU workloads, about 25% for GPU workloads. The efficiency depends on the arithmetic intensity, however energy savings are always achieved. ## Barbora -- GitLab