Workloads of all types, across any hypervisor will experience performance degradation and risk. You can see here from a view in an active VDI environment that peaks of CPU utilization are occurring and that has been correlated to user experience issues.
CPU queue depth increases during those peak times resulting in application delays. This is critical for latency-sensitive workloads and will have a negative performance effect on workloads of any kind. Not only is the direct application effected, but any VM which is causing high CPU usage and queueing will now impact every VM, every container, and every application that is on that same physical host.
You now have a risk that one of your application servers could be taking your call center application offline or stopping transaction flow into your databases applications. That wait time translates to slower queries, poorer SQL performance, and any applications depending on that SQL server will suffer as a result.
VMware even clearly documents the risk in their page guide on recommended practices for architecting SQL for vSphere:. You can see the real impact of both peaks of utilization and queuing for multi-vCPU systems.
CPU utilization peaks and high CPU queue depths cannot be solved by simply relying on the native schedulers. This has proven to be true whether on virtualization or containerized platforms. You can now see the full impact which led to Turbonomic automating moving and sizing of resources that resulted in a performance improvement from the application down across the entire cluster that was being automated.
All of this was while improving performance for the entire application environment and the resulting host reallocation restarted a new data center project which was frozen because of a lack of ability to get new servers. A real example came up recently which highlighted how Turbonomic solves the problem of performance for any application, including SQL servers.
The IT Ops team investigates the host and does not see any consistent patterns or active issues at the time they get the call from the DBA. Nobody is sure how to resolve the issue with the native tools and data.
While this may seem counter-intuitive, the Turbonomic platform identified that the SQL server will have consistently better application-layer performance because of less CPU queue wait time for a CPU instruction. Both the DBA and operations teams now have the precise decision, action, and analytics to back their choice. When the clock speed of processors came close to the heat barrier, manufacturers changed the architecture of processors and started producing processors with multiple CPU cores.
To avoid confusion between physical processors and logical processors or processor cores, some vendors refer to a physical processor as a socket. A CPU core is the part of a processor containing the L1 cache. Basically, a core can be considered as a small processor built into the main processor that is connected to a socket.
Applications should support parallel computations to use multicore processors rationally. Hyper-threading is a technology developed by Intel engineers to bring parallel computation to processors that have one processor core. The debut of hyper-threading was in when the Pentium 4 HT processor was released and positioned for desktop computers. An operating system detects a single-core processor with hyper-threading as a processor with two logical cores not physical cores.
Similarly, a four-core processor with hyper-threading appears to an OS as a processor with 8 cores. The more threads run on each core, the more tasks can be done in parallel. Modern Intel processors have both multiple cores and hyper-threading.
Hyper-threading is usually enabled by default and can be enabled or disabled in BIOS. A vCPU is a virtual processor that is configured as a virtual device in the virtual hardware settings of a VM.
A virtual processor can be configured to use multiple CPU cores. A vCPU is connected to a virtual socket. CPU overcommitment is the situation when you provision more logical processors CPU cores of a physical host to VMs residing on the host than the total number of logical processors on the host. NUMA non-uniform memory access is a computer memory design used in multiprocessor computers. The idea is to provide separate memory for each processor unlike UMA, where all processors access shared memory through a bus.
At the same time, a processor can access memory that belongs to other processors by using a shared bus all processors access all memory on the computer. A CPU has a performance advantage of accessing own local memory faster than other memory on a multiprocessor computer. These basic architectures are mixed in modern multiprocessor computers. Processors are grouped on a multicore CPU package or node. Processors that belong to the same node share access to memory modules as with the UMA architecture.
Also, processors can access memory from the remote node via a shared interconnect. Processors do so for the NUMA architecture but with slower performance. This memory access is performed through the CPU that owns that memory rather than directly. An example. Each CPU has 6 processor cores. This server contains two NUMA nodes. The total number of physical CPU cores on a host machine is calculated with the formula:. If hyper-threading is supported, calculate the number of logical processor cores by using the formula:.
Finally, use a single formula to calculate available processor resources that can be assigned to VMs:. For example, if you have a server with two processors with each having 4 cores and supporting hyper-threading, then the total number of logical processors that can be assigned to VMs is. As for virtual machines, due to hardware emulation features, they can use multiple processors and CPU cores in their configuration for operation. For example, if you configure a VM to use 2 vCPUs with 2 cores when you have a physical processor whose clock speed is 3.
The ultimate limit is 1 band per core. So, for example, if you have bands, you cannot run on more than cores and expect it to work well. What I have seen, in my scaling tests, though, is that 1 band per core is too little work for a modern processor.
So how does this relate to the rule of thumb above? By apply it, you will arrive at a number of bands per core equal to the average number of valence electrons per atom in your calculation. Example 1 : We have a cell with bands and a cluster with compute nodes having 16 cores per node.
Example 2 : Suppose that you want to speed up the calculation in the previous example. You need the results fast, and care less about efficiency in terms of the number of core hours spent. So it seems like cores is the maximum number possible. But what you can do is to take these MPI processes and spread them out over more compute nodes. This will improve the memory bandwidth available to each MPI process, which usually speed things up. It could be faster, if the extra communication overhead is not too large.
The next step is to consider the number of k-points. VASP can treat each k-point independently. The number of k-point groups that run in parallel is controlled by the KPAR parameter.
The upper limit of KPAR is obviously the number of k-points in your calculation.
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