#DIGITALPERSONA LOCAL AGENT MEMORY SOFTWARE#

A well-known software prefetching technology is SSE (Streaming SIMD Extension SIMD: Single Instruction Multiple Data) SSE provides hints to the CPU which data to prefetch for an instruction. The Xeon microarchitecture can make use of both hardware and software prefetching. Performance improvements up to 30% have been quoted by Intel. ItвЂ™s the job of the prefetcher to load data into the cache before the core needs it. In order to improve performance, data can be speculatively loaded into the L1 and L2 cache, this is called prefetching.

It must have all the data contained in the L2 or L1 cache. The reason why itвЂ™s also in the LCC is because the LCC is designed as an inclusive cache. When data is fetched from memory it fills all cache levels on the way to the core (LCC->L2->L1). However, itвЂ™s likely to have the same data and instructions as itвЂ™s bigger (less evictions). That means that it does not have to contain all the bits that is present in the L1 cache (instructions and data). The L2 cache (256KB) is shared by instructions and data (Unified) and is considered to be an exclusive cache. The L1 cache is split into two separate elements, the Instruction cache (32KB) and the data cache (L1D) (32KB). In comparison, it takes roughly 190 cycles to get the data from local memory while it could take the CPU a whopping 310 cycles to load the data from remote memory.Д®ach core has a dedicated L1 and L2 cache, this is referred to as private cache as no other core can overwrite the cache lines, the LLC is shared between cores. L1 is the fastest cache and it typically takes the CPU 4 cycles to load data from the L1 cache, 12 cycles to load data from the L2 cache and between 26 and 31 cycles to load the data from е…ѓ cache. Although they are all located on the CPU die, there are differences in latency between L1, L2 and LLC. The hierarchy exists of an L1, L2 and a distributed LLC accessed via the on-die scalable ring architecture. Sandy Bridge (v1) introduced a new cache architecture. A great deal of memory performance (bandwidth and latency) depends on the snoop protocol. The interesting thing is that with todayвЂ™s multicore CPU architecture, cache coherency manifest itself within the CPU package as well as cache coherency between CPU packages.

#DIGITALPERSONA LOCAL AGENT MEMORY UPDATE#

To ensure that the local cache is up to date, the snoopy bus protocol was invented, which allowed caches to listen in on the transport of these вЂњvariablesвЂќ to any of the CPU and update their own copies of these variables if they have them. This means that a memory system of a multi-CPU system is coherent if CPU 1 writes to a memory address (X) and later on CPU2 reads X, and no other writes happened to X in between, CPU 2 read operation returns the value written by CPU 1 write operation. Therefore, it must be assured that the caches that provide these variables are also consistent in this respect. The term вЂњCache CoherentвЂќ refers to the fact that for all CPUs any variable that is to be used must have a consistent value.

However, a great deal of the efficiency of a NUMA system depends on the scalability and efficiency of the cache coherence protocol! When researching the older material of NUMA, todayвЂ™s architecture is primarily labeled as ccNUMA, Cache Coherent NUMA. Locating memory close to CPUs increases scalability and reduces latency if data locality occurs. Unfortunately, the importance of cache coherency in this architecture is mostly ignored. When people talk about NUMA, most talk about the RAM and the core count of the physical CPU.

Digitalpersona local agent memory