Cpu efficiency is very less in case of

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I do fair bit of dabbling in performance focused programming. Typically, most of the techniques I have been taught and am aware of pertain to conserving RAM. That being said, I recently addressing the question here Where I was told: it is usually the CPU that runs out of speed before memory is exhausted We did some testing and it seemed packing/unpacking does conserve RAM, but definitely slows down performance. But as I have said, most of the typical performance 'rules' that I've seen, deal with conserving RAM. One of the major topics in program speed, for example, is dynamic memory allocation, which is also focused on RAM conservation. What I want to know is: What makes code CPU efficient? Do lower-level languages like C have more flexibility for CPU efficiency? If so, why/how? For the sake of simplicity, lets exclude discussion on assembler languages because they are beyond the scope of this question. Profiler First things first, when you go beyond glaring algorithmic inefficiencies, you want to find yourself a nice profiler. A profiler has several benefits: • Shows you precise measurements (where the time is spent, cache misses, branch mispredictions, etc). • Chasing down your top hotspots will tend to rapidly accelerate your learning process and intuition of what causes bottlenecks at a micro level (ex: memory hierarchy and branching). • Will make you a better prioritizer. It'll also teach you what code doesn't need optimization which means you can focus there on other...

Chapter 4. How Go Uses the CPU Resource (or Two) One of the most useful abstractions we can make is to treat properties of our hardware and infrastructure systems as resources. CPU, memory, data storage, and the network are similar to resources in the natural world: they are finite, they are physical objects in the real world, and they must be distributed and shared between various key players in the ecosystem. Susan J. Fowler, Production-Ready Microservices (O’Reilly, 2016) As This might sound simple, but in modern computers, these resources interact with each other in a complex, nontrivial way. Furthermore, more than one process is using these resources, so our program does not use them directly. Instead, these resources are managed for us by an operating system. If that wasn’t complex enough, especially in cloud environments, we often “virtualize” the hardware further so it can be shared across many individual systems in an isolated way. That means there are methods for “hosts” to give access to part of a single CPU or disk to a “guest” operating system that thinks it’s all the hardware that exists. In the end, operating systems and virtualization mechanisms create layers between our program and the actual physical devices that store or compute our data. To understand how to write efficient code or improve our program’s efficiency effectively, we have to learn the characteristics, purpose, and limits of the typical computer resources like CPU, differ...

This section needs additional citations for Please help ( February 2018) ( ( In program optimization, code optimization, or software optimization, is the process of modifying a software system to make some aspect of it work more General [ ] Although the word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system. A system can generally be made optimal not in absolute terms, but only with respect to a given quality metric, which may be in contrast with other possible metrics. As a result, the optimized system will typically only be optimal in one application or for one audience. One might reduce the amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premium, one might deliberately choose a slower Even for a given quality metric (such as execution speed), most methods of optimization only improve the result; they have no pretense of producing optimal output. Levels of optimization [ ] Optimization can occur at a number of levels. Typically the higher levels have greater impact, and are harder to change later on in a project, requiring significant changes or a complete rewrite if they need to be changed. Thus optimization can typically proceed via refinement from higher to lower, with initial gains being larger and achieved with less work, and later gains being smaller and requiring more work. However, in som...

cpu env config CPU(s): 72 On-line CPU(s) list: 0-71 Thread(s) per core: 2 synthesisrealtime world: 30 threads：every threads cpu efficiency is 100%， cpu efficiency is very high 60 threads：every threads cpu efficiency is 70%，cpu efficiency is very low 140 threads：every threads cpu efficiency is 65%，cpu efficiency is very low synthesis world: 30 threads：every threads cpu efficiency is 100%， cpu efficiency is very high 60 threads：every threads cpu efficiency is 100%，cpu efficiency is very high 140 threads：every threads cpu efficiency is100%，cpu efficiency is very high Cpu efficiency is very low，when multithread use synthesisrealtime world（more than a certain number of concurrency） Cpu efficiency still keep very high，when multithread use synthesis world（more than a certain number of concurrency） Under multithreading, synthesisrealtime world performance is not as good as synthesis world(such as response time, real-time rate etc)，The main reason is that synthesisrealtime world cannot make full use of cpu resources；How to make streaming synthesisrealtime world response time faster than non-streaming synthesis world? Thank you for your comment. It is difficult to identify the problem based on only the given information completely. Generally, real-time synthesis is sequentially carried out because the past speech parameter affects the current waveform. When you have generated speech parameters sequentially, the CPU efficiency may decrease. In this case, if the computational cost for...

In general use, should I bet on memory efficiency or processor efficiency? In the end, I know that must be according to software/hardware specs. but I think there's a general rule when there's no boundaries. Example 01 (memory efficiency): int n=0; if(n < getRndNumber()) n = getRndNumber(); Example 02 (processor efficiency): int n=0, aux=0; aux = getRndNumber(); if(n < aux) n = aux; They're just simple examples and wrote them in order to show what I mean. Better examples will be well received. Thanks in advance. I'm going to wheel out the universal performance question trump card and say "neither, bet on correctness". Write your code in the clearest possible way, set specific measurable performance goals, measure the performance of your software, profile it to find the bottlenecks, and then if necessary optimise knowing whether processor or memory is your problem. (As if to make a case in point, your 'simple examples' have different behaviour assuming getRndNumber() does not return a constant value. If you'd written it in the simplest way, something like n = max(0, getRndNumber()) then it may be less efficient but it would be more readable and more likely to be correct.) Edit: To answer Dervin's criticism below, I should probably state why I believe there is no general answer to this question. A good example is taking a random sample from a sequence. For sequences small enough to be copied into another contiguous memory block, a partial Fisher-Yates shuffle which favours c...

I watched "Efficiency Cores", and they have been named Efficiency Cores and the rest of the cores in the CPU are "Performance Cores", which — to no surprise — have been named Performance Cores. When new microprocessors are released it can be really hard to know what names, and numbers, are actually based in computer science, and which ones are just a marketing attempt to make the chips look good. In other-words, what I would like to know: Is there actually anything to the performance, and efficiency cores, or is it just a marketing ploy? And if the two core are not just a marketing ploy, and having cores for efficiency and cores for performance is going to make a difference, then what exactly will the efficiency cores do that will be different from what the performance cores do? It's Intel's version of totally made up, not intended even as a guess about Alder Lake. Intel has some marketing graphs). The Performance (P) cores are next-gen Ice Lake cores, like in mainstream desktop/laptop/server. Specifically, (Hybrid chips are new and x86 hardware / software ecosystem The Efficiency (E) cores are The earliest Silvermont-family CPUs were pretty low-end, with out-of-order exec (for integer only, not FP/SIMD), but with a much smaller "window" to find instruction-level parallelism, and much narrower pipeline (fewer instructions decoded or executed in parallel). They were the successor to the original Atom, used in netbooks and some server appliance things such as NAS boxes. But ...