Update energy.md

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