hpc

问题 I want to multiply matrices by D*W', where W' is a transposed version of W. While I'll use DGEMM I'l figured out with the help of @IanBush that the LDB in this case should be number of rows of matrix W instead of number of columns. The code for this case is Call dgemm('n', 't', N1, M1, N1, 1.0_wp, D, N1, W, M1, 0.0_wp, c, n1) where n1 and m1 are dimensions of my matrices Dimensions of the matrices: W = M1*N1 D = N1*N1 As in official documentation it says LDB is INTEGER On entry, LDB specifies

问题 I want to know if a program that I am using and which requires a lot of memory is limited by the memory bandwidth. When do you expect this to happen? Did it ever happen to you in a real-life scenario? I found several articles discussing this issue, including: http://www.cs.virginia.edu/~mccalpin/papers/bandwidth/node12.html http://www.cs.virginia.edu/~mccalpin/papers/bandwidth/node13.html http://ispass.org/ucas5/session2_3_ibm.pdf The first link is a bit old, but suggests that you need to

问题 I want to change the attributes (walltime and queue) of several (idle) jobs scheduled on a cluster. When I do (where 1234 is the job id): qalter -l walltime=24:00:00 -q newQueue 1234 I get the following error : qalter: illegally formed job identifier: newQueue What can I do? 回答1: You need to do this in two steps: qalter to change the walltime qalter -l walltime=24:00:00 1234 qmove to move the job to newQueue qmove newQueue 1234 This order might be important, if newQueue is restricted for a

问题 I want to change the attributes (walltime and queue) of several (idle) jobs scheduled on a cluster. When I do (where 1234 is the job id): qalter -l walltime=24:00:00 -q newQueue 1234 I get the following error : qalter: illegally formed job identifier: newQueue What can I do? 回答1: You need to do this in two steps: qalter to change the walltime qalter -l walltime=24:00:00 1234 qmove to move the job to newQueue qmove newQueue 1234 This order might be important, if newQueue is restricted for a

问题 I wish to allocate a vector and use it's data pointer to allocate a zero copy buffer on the GPU. There is this cl_arm_import_memory extension which can be used to do this. But I am not sure wether its supported for all mali midgard OpenCL drivers or not. I was going through this link and I am quite puzzled by the following lines : - If the extension string cl_arm_import_memory_host is exposed then importing from normal userspace allocations (such as those created via malloc) is supported.

问题 I wish to allocate a vector and use it's data pointer to allocate a zero copy buffer on the GPU. There is this cl_arm_import_memory extension which can be used to do this. But I am not sure wether its supported for all mali midgard OpenCL drivers or not. I was going through this link and I am quite puzzled by the following lines : - If the extension string cl_arm_import_memory_host is exposed then importing from normal userspace allocations (such as those created via malloc) is supported.

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . I am wondering how I might be able to run 500 parallel jobs in R using the Rscript function. I currently have an R file that has the header on top: args <- commandArgs(TRUE) B <- as.numeric(args[1]) Num.Cores <- as.numeric(args[2]) Outside of the R file, I wish to pass which of

问题 I need to assign N entities (each with possible parents and possible children) to M computation nodes while satisfying the following optimization conditions: Children of an entity want to be assigned to the same computation node (to maximize data locality among siblings) The distribution of entities should be as even as possible (i.e. no overtaxing of a single node). I'm looking for some suggestions on heuristic methods to solve this problem. I've read http://en.wikipedia.org/wiki/Assignment

问题 I need to assign N entities (each with possible parents and possible children) to M computation nodes while satisfying the following optimization conditions: Children of an entity want to be assigned to the same computation node (to maximize data locality among siblings) The distribution of entities should be as even as possible (i.e. no overtaxing of a single node). I'm looking for some suggestions on heuristic methods to solve this problem. I've read http://en.wikipedia.org/wiki/Assignment

问题 I have a computation task which is split in several individual program executions, with dependencies. I'm using Condor 7 as task scheduler (with the Vanilla Universe, due do constraints on the programs beyond my reach, so no checkpointing is involved), so DAG looks like a natural solution. However some of the programs need to run on the same host. I could not find a reference on how to do this in the Condor manuals. Example DAG file: JOB A A.condor JOB B B.condor JOB C C.condor JOB D D.condor