kepler | 易学教程

为什么Map.get（Object key）不是（完全）泛型的原因是什么

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决定在 java.util.Map<K, V> 接口中没有完全通用的get方法的背后原因是什么？为了澄清问题，该方法的签名是 V get(Object key) 代替 V get(K key) 我想知道为什么（与 remove, containsKey, containsValue ）。 #1楼这是 Postel法则的一种应用， “对您的工作要保守，对别人接受的东西要宽松”。不论类型如何，都可以执行相等性检查。 equals 方法在 Object 类上定义，并接受任何 Object 作为参数。因此，对于密钥等效性以及基于密钥等效性的操作来说，接受任何 Object 类型都是有意义的。映射返回键值时，通过使用type参数，它可以保存尽可能多的类型信息。 #2楼还有一个重要的原因，因为它破坏了Map，因此在技术上无法完成。 Java具有像 <? extends SomeClass> <? extends SomeClass> 。标记为此类引用可以指向使用 <AnySubclassOfSomeClass> 签名的类型。但是多态泛型使该引用变为只读。编译器仅允许您将泛型类型用作方法的返回类型（如简单的getter），但会阻止使用泛型为参数的方法（如普通的setter）。这意味着您是否编写 Map<? extends KeyType, ValueType> Map

Are GPU Kepler CC3.0 processors not only pipelined architecture, but also superscalar? [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 4 years ago . In the documentation for CUDA 6.5 has written: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#ixzz3PIXMTktb 5.2.3. Multiprocessor Level ... 8L for devices of compute capability 3.x since a multiprocessor issues a pair of instructions per warp over one clock cycle for four warps at a time, as

Modified Nvidia Maxwell, increased global memory instruction count

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问题 I had an experiment on both GTX760(Kepler) and GTX750Ti(Maxwell) using benchmarks(Parboil, Rodinia). Then I analyzed results using Nvidia visual profiler. In most of the applications, the number of global instructions are enormously increased up to 7-10 times on Maxwell architecture. spec. for both graphic cards GTX760 6.0Gbps 2048MB 256bit 192.2 GB/s GTX750Ti 5.4Gbps 2048MB 128bit 86.4Gb/s Ubuntu 14.04 CUDA driver 340.29 toolkit 6.5 I compiled the benchmark application(No modification) then

“Global Load Efficiency” over 100%

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问题 I have a CUDA program in which threads of a block read elements of a long array in several iterations and memory accesses are almost fully coalesced. When I profile, Global Load Efficiency is over 100% (between 119% and 187% depending on the input). Description for Global Load Efficiency is " Ratio of global memory load throughput to required global memory load throughput. " Does it mean that I'm hitting L2 cache a lot and my memory accesses are benefiting from it? My GPU is GeForce GTX 780

Kepler RCP CoolBar + PerspectiveBar appearance

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问题 The coolbars on all Kepler RCP applications look just terrible. This a project created from scratch on Windows 8. What is it with that thick black line sorrounding the coolbar? Can it be style-hacked with CSS? From my testing, it seems that it appears only when the perspective bar is present. 回答1: I think the CSS for this is the eclipse-perspective-keyline-color in the #PerspectiveSwitcher selector. 回答2: You can try to add this dependencies in your product file: org.eclipse.e4.ui.css.core ,

The efficiency and performance of ILP for the NVIDIA Kepler architecture

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问题 Quoting the "Kepler Tuning Guide" provided by NVIDIA: Also note that Kepler GPUs can utilize ILP in place of thread/warp-level parallelism (TLP) more readily than Fermi GPUs can. In my opinion, the following code snippet a = .....; a2 = f(a); a3 = g(a2); can be improved as follows a = ...; b = ....; a2 = f(a); b2 = f(b); a3 = g(a2); b3 = g(b2); So in my projects, I have a section of code as follows (example 1) if(x < src.cols && y < src.rows) { if(!mask(y,x)) { src.ptr(y)[x] = make_short4(0,0

Are GPU Kepler CC3.0 processors not only pipelined architecture, but also superscalar? [closed]

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In the documentation for CUDA 6.5 has written: http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#ixzz3PIXMTktb 5.2.3. Multiprocessor Level ... 8L for devices of compute capability 3.x since a multiprocessor issues a pair of instructions per warp over one clock cycle for four warps at a time, as mentioned in Compute Capability 3.x. Does this mean that the GPU Kepler CC3.0 processors are not only pipelined architecture, but also superscalar? Pipelining - these two sequences execute in parallel (different operations at one time): LOAD [addr1] -> ADD -> STORE [addr1] -> NOP NOP ->

说好不哭！数据可视化深度干货，前端开发下一个涨薪点在这里~

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随着互联网在各行各业的影响不断深入，数据规模越来越大，各企业也越来越重视数据的价值。作为一家专业的数据智能公司，个推从消息推送服务起家，经过多年的持续耕耘，积累沉淀了海量数据，在数据可视化领域也开展了深入的探索和实践。个推的数据可视化探索和应用由需求出发，从基于开源平台到结合个性化需求进行定制化开发，打造出个推实时消息推送下发图，人群分布热力图等优秀数据可视化作品。这一过程中，个推积累沉淀了大量的数据可视化组件，打磨了自己的数据可视化技术能力。其中，个推热力图正应用在智慧城市、人口空间规划、公共服务等领域，为其提供强大的数据支撑。个推消息下发图个推打造的湖滨商圈区域人口热力图本文就和大家分享一下个推的数据可视化实践、遇到的问题及解决思路，希望大家能从中有所受益。一、数据可视化的构成数据可视化由四类可视化元素构成：背景信息、标尺、坐标系、视觉暗示。 1.1 背景信息背景信息就是标题、度量单位、注释等附加类的信息。主要是为了帮助大屏受众更好地理解相关背景信息，即5W信息：何人（who）、何事（what）、何时（when）、何地（where）、为何（why）。 1.2 标尺标尺主要用来衡量不同方向和维度上的数据大小，常用的有数字标尺、分类标尺、时间标尺等，类似我们熟悉的刻度。 1.3 坐标系坐标系有一个结构化的空间，还有指定图形和颜色画在哪里的规则，用于编码数据的时候

个推数据可视化之人群热力图、消息下发图前端开发实践

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Concurrent, unique kernels on the same multiprocessor?

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问题 Is it possible, using streams, to have multiple unique kernels on the same streaming multiprocessor in Kepler 3.5 GPUs? I.e. run 30 kernels of size <<<1,1024>>> at the same time on a Kepler GPU with 15 SMs? 回答1: On a compute capability 3.5 device, it might be possible. Those devices support up to 32 concurrent kernels per GPU and 2048 threads peer multi-processor. With 64k registers per multi-processor, two blocks of 1024 threads could run concurrently if their register footprint was less

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