CUDA and cuda ProgrammingCUDA SHARED MEMORY
Shared memory has some introductions in previous blog posts. This section focuses on its content. In the global Memory section, Data Alignment and continuity are important topics. When L1 is used, alignment can be ignored, but non-sequential Memory acquisition can still reduce performance. Dependent on the nature of algorithms, in some cases, non-continuous access
CUDA 5, CUDAGPU Architecture
SM (Streaming Multiprocessors) is a very important part of the GPU architecture. The concurrency of GPU hardware is determined by SM.
Taking the Fermi architecture as an example, it includes the following main components:
CUDA cores
Shared Memory/L1Cache
Register File
Load/Store Units
Special Function Units
Warp Scheduler
Each SM in the GPU is designed to support hundred
Use Python to write the CUDA program, and use python to write the cuda Program
There are two ways to write a CUDA program using Python:
* Numba* PyCUDA
Numbapro is no longer recommended. It is split and integrated into accelerate and Numba.
Example
Numba
Numba optimizes Python code through the JIT mechanism. Numba can optimize the hardware environment of the Loca
write in front
The content is divided into two parts, the first part is translation "Professional CUDA C Programming" section 2. The timing YOUR KERNEL in CUDA programming model, and the second part is his own experience. Experience is not enough, you are welcome to add greatly.
Cuda, the pursuit of speed ratio, want to get accurate time, the timing function is
RHEL/CentOS PXE diskless workstation, rhelcentospxe diskless workstation
• Introduction to PXE diskless workstation Systems
A pxe diskless workstation system is a LAN system consisting of one or more "system servers" and multiple "PXE clients (diskless workstation)" connect
Cuda Memory Model:
GPU chip: Register, shared memory;
Onboard memory: local memory, constant memory, texture memory, texture memory, global memory;
Host memory: host memory, pinned memory.
Register: extremely low access latency;
Basic Unit: register file (32bit/each)
Computing power 1.0/1.1 hardware: 8192/Sm;
Computing power 1.2/1.3 hardware: 16384/Sm;
The register occupied by each thread is limited. Do not assign too many private variables to it dur
CUDA 6, CUDAWarp
Logically, all threads are parallel. However, from the hardware point of view, not all threads can be executed at the same time. Next we will explain some of the essence of warp.Warps and Thread Blocks
Warp is the basic execution unit of SM. A warp contains 32 parallel threads, which are executed in SMIT mode. That is to say, all threads execute the same command, and each thread uses its own data to execute the command.
A block can be
Document directory
Function qualifier
Variable type qualifier
Execute Configuration
Built-in Variables
Time Functions
Synchronous Functions
1. Parallel Computing
1) Single-core command-level parallel ILP-enables the execution unit of a single processor to execute multiple commands simultaneously
2) multi-core parallel TLP-integrate multiple processor cores on one chip to achieve line-level parallel
3) multi-processor parallelism-Install multiple processors on a single circuit board and i
Summary of accelerated installation of Amber11 + AmberTools1.5 + CUDA
The following installation method is based on some of the previous posts on the Forum simulated by the numerator. The installation and testing can be successful as long as the operation is correct. Considering that Amber11 is generally installed on clusters, the intel compiler and Openmpi parallel tool are used for installation. You need to purchase the Amber11 software to obtain th
Cuda register array resolution, cuda register
About cuda register array
When performing Parallel Optimization on some algorithms based on cuda, in order to improve the running speed of the algorithm as much as possible, sometimes we want to use register arrays to make the algorithm fly fast, but the effect is always u
Win10 with CMake 3.5.2 and vs update1 compiling GPU version (Cuda 8.0, CUDNN v5 for Cuda 8.0) Open compile release and debug version with VS 2015 See the example on the net there are three inside the project Folders include (Include directories containing Mxnet,dmlc,mshadow)Lib (contains Libmxnet.dll, libmxnet.lib, put it in vs. compiled)Python (contains a mxnet,setup.py, and build, but the build contains t
Today we have a few gains, successfully running the array summation code: Just add the number of n sumEnvironment: cuda5.0,vs2010#include "cuda_runtime.h"#include "Device_launch_parameters.h"#include cudaerror_t Addwithcuda (int *c, int *a);#define TOTALN 72120#define Blocks_pergrid 32#define THREADS_PERBLOCK 64//2^8__global__ void Sumarray (int *c, int *a)//, int *b){__shared__ unsigned int mycache[threads_perblock];//sets the shared memory within each block threadsperblock==blockdim.xint i = t
Install cuda6.5 + vs2012, the operating system is win8.1 version, first of all the next GPU-Z detected a bit:
It can be seen that this video card is a low-end configuration, the key is to look at two:
Shaders = 384, also known as Sm, or the number of core/stream processors. The larger the number, the more parallel threads are executed, and the larger the computing workload per unit time.
Buswidth = 64bit. The larger the value, the faster the data processing speed.
Next let's take a look at the
In this paper, the basic concepts of CUDA parallel programming are illustrated by the vector summation operation. The so-called vector summation is the addition of the corresponding element 22 in the two array data, and the result is saved in the third array. As shown in the following:1. CPU-based vector summation:The code is simple:#include the use of the while loop above is somewhat complex, but it is intended to allow the code to run concurrently o
CUDA 3, CUDAPreface
The thread organization form is crucial to the program performance. This blog post mainly introduces the thread organization form in the following situations:
2D grid 2D block
Thread Index
Generally, a matrix is linearly stored in global memory and linear with rows:
In kernel, the unique index of a thread is very useful. To determine the index of a thread, we take 2D as an example:
Thread and block Indexes
Element coordinates
Hya. io-Web-based digital audio workstation and hya. io audio workstation
Hya. io is based onWebAudio Application, supporting hardware MIDI through Web MIDI, audio synthesizer, audio sequence and a large number of plug-ins. You can add a plug-in to the work zone to connect it to the route audio for playing and experimenting.
HYA supports AMD and ES6 modules. They use a simple JSON object to describe the plu
VMware Workstation is incompatible with Hyper-V. Please remove the Hyper-V role from the system before running VMware Workstation.VMware Workstation with the Hyper-V not compatible. Please remove the Hyper-V role from the system before running VMware Workstation. The first step:Step Two:When you are finished, you are prompted to restart, and then you can install
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