E-mail: tianliu@mpib-berlin.mpg.de or lbertram@molgen.mpg.de Received 28 January 2014; revised 1 May 2014; accepted 21 May 2014 Role of CTNNBL1 in human memory T Liu et al 2 Assessment of episodic memory performance principal components (PCs) were computed again for the 1743 remaining ...
(s■ Two 8-bit index registers c■ 16-bit stack pointer u■ Low power modes rod■ Maskable hardware interrupts ■ Non-maskable software interrupt te P5.3 CPU REGISTERS leThe six CPU registers shown in Figure 1 are not sopresent in the memory mapping and are accessed bby specific ...
adata-centricperformance tool requires the mapping of memory references to the corresponding symbolic names of the data structure, which is non trivial, especially for mapping local variables and dynamically allocated data structures. In this paper we describe with examples the algorithms and extensions ...
We evaluate the translator on representative benchmarks of this class and compare their performance against hand-written MPI variants. In all but one case, our translated versions perform close to the hand-written variants.doi:10.1007/978-3-642-36036-7_1Okwan Kwon...
When Bootstrap mode is selected, the Test-Flash Block B0TF (8 Kbyte) appears at address 00'0000h: refer to Chapter 5: Internal Flash memory on page 24 for more details on memory mapping in boot mode. The summary of address range for IFlash is the following: t(s)Table 2. Summary of...
The cost of memory registration can be significant and impacts performance when done dynamically in code. Consequently, many high-performance messaging libraries (e.g., MPI) try to preregister memory, so that a pool of buffers can be used for low-latency communication. For large bulk data ...
Each MPI-rank running on a GPU increases the use of GPU-memory. Relion tries to distribute load over multiple GPUs to increase performance, but doing this in a general and memory-efficient way is difficult. Check the device-mapping presented at the beginning of each run, and be particularly...
Hi, We found that when using the wait mode with shm:ofa fabrics, the processes of the MPI program use more memory than the other configurations. In
(eg. map a file A1 on buffer B, pin B, tell cuda that B was pinned, unmap A1 and map file A2 instead, at the same location). This is really a problem as it’s almost impossible to detect such memory mapping change from the cuda user-apps driver, since it has no clue of those...
For example, in a 1:1 mapping of the time/neuron array to the memory space allows processors to address each and every local address in the message passing domain individually and directly. This results in a large memory space, but requires no address computation by the processor. Alternatively...