unified-memory

问题 I am trying to implement a CUDA program that uses Unified Memory. I have two unified arrays and sometimes they need to be updated atomically. The question below has an answer for a single GPU environment but I am not sure how to extend the answer given in the question to adapt in multi-GPU platforms. Question: cuda atomicAdd example fails to yield correct output I have 4 Tesla K20 if you need this information and all of them updates a part of those arrays that must be done atomically. I would

问题 While using CudaMallocManaged() to allocate an array of structs with arrays inside, I'm getting the error "out of memory" even though I have enough free memory. Here's some code that replicates my problem: #include <iostream> #include <cuda.h> #define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); } inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) { if (code != cudaSuccess) { fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file,

问题 What I want is to be able to monitor Spark execution memory as opposed to storage memory available in SparkUI. I mean, execution memory NOT executor memory . By execution memory I mean: This region is used for buffering intermediate data when performing shuffles, joins, sorts and aggregations. The size of this region is configured through spark.shuffle.memoryFraction (default0.2). According to: Unified Memory Management in Spark 1.6 After intense search for answers I found nothing but