Dynamic Scratch-Pad Memory Management for Irregular Array Access Patterns

2006 ◽  
Author(s):  
G. Chen ◽  
O. Ozturk ◽  
M. Kandemir ◽  
M. Karakoy
Keyword(s):  
Memory Management ◽  
Scratch Pad Memory ◽  
Access Patterns

Adaptive Scratch Pad Memory Management for Dynamic Behavior of Multimedia Applications

2009 ◽  
Vol 28 (4) ◽  
pp. 554-567 ◽  
Author(s):  
Doosan Cho ◽  
S. Pasricha ◽  
I. Issenin ◽  
N.D. Dutt ◽  
Minwook Ahn ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Memory Management ◽  
Multimedia Applications ◽  
Scratch Pad Memory

Compiler-assisted dynamic scratch-pad memory management with space overlapping for embedded systems

2010 ◽  
Vol 41 (7) ◽  
pp. 737-752 ◽  
Author(s):  
Yanqin Yang ◽  
Haijin Yan ◽  
Zili Shao ◽  
Minyi Guo
Keyword(s):  
Embedded Systems ◽  
Memory Management ◽  
Scratch Pad Memory

scholarly journals ExSeisDat: A set of parallel I/O and workflow libraries for petroleum seismology

2018 ◽  
Vol 73 ◽  
pp. 74 ◽  
Author(s):  
Meghan A. Fisher ◽  
Pádraig Ó. Conbhuí ◽  
Cathal Ó. Brion ◽  
Jean-Thomas Acquaviva ◽  
Seán Delaney ◽  
...  
Keyword(s):  
Seismic Data ◽  
Memory Management ◽  
File System ◽  
File Systems ◽  
Data Access ◽  
System Level ◽  
Data Set ◽  
Extreme Scale ◽  
Data Access Patterns ◽  
Access Patterns

Seismic data-sets are extremely large and are broken into data files, ranging in size from 100s of GiBs to 10s of TiBs and larger. The parallel I/O for these files is complex due to the amount of data along with varied and multiple access patterns within individual files. Properties of legacy file formats, such as the de-facto standard SEG-Y, also contribute to the decrease in developer productivity while working with these files. SEG-Y files embed their own internal layout which could lead to conflict with traditional, file-system-level layout optimization schemes. Additionally, as seismic files continue to increase in size, memory bottlenecks will be exacerbated, resulting in the need for smart I/O optimization not only to increase the efficiency of read/writes, but to manage memory usage as well. The ExSeisDat (Extreme-Scale Seismic Data) set of libraries addresses these problems through the development and implementation of easy to use, object oriented libraries that are portable and open source with bindings available in multiple languages. The lower level parallel I/O library, ExSeisPIOL (Extreme-Scale Seismic Parallel I/O Library), targets SEG-Y and other proprietary formats, simplifying I/O by internally interfacing MPI-I/O and other I/O interfaces. The I/O is explicitly handled; end users only need to define the memory limits, decomposition of I/O across processes, and data access patterns when reading and writing data. ExSeisPIOL bridges the layout gap between the SEG-Y file structure and file system organization. The higher level parallel seismic workflow library, ExSeisFlow (Extreme-Scale Seismic workFlow), leverages ExSeisPIOL, further simplifying I/O by implicitly handling all I/O parameters, thus allowing geophysicists to focus on domain-specific development. Operations in ExSeisFlow focus on prestack processing and can be performed on single traces, individual gathers, and across entire surveys, including out of core sorting, binning, filtering, and transforming. To optimize memory management, the workflow only reads in data pertinent to the operations being performed instead of an entire file. A smart caching system manages the read data, discarding it when no longer needed in the workflow. As the libraries are optimized to handle spatial and temporal locality, they are a natural fit to burst buffer technologies, particularly DDN’s Infinite Memory Engine (IME) system. With appropriate access semantics or through the direct exploitation of the low-level interfaces, the ExSeisDat stack on IME delivers a significant improvement to I/O performance over standalone parallel file systems like Lustre.

scholarly journals Memory access behavior of dynamically allocated data structures and programs with irregular access patterns

2021 ◽  
Author(s):  
Zhen Yu
Keyword(s):  
Data Structures ◽  
Memory Management ◽  
Memory Hierarchy ◽  
Research Work ◽  
Memory Access ◽  
Mathematical Formula ◽  
Dynamic Memory ◽  
Dynamic Memory Management ◽  
Management Policies ◽  
Access Patterns

With the development of modern computers, memory latencies have become a key bottleneck for the performance of computer systems. Since then, much research work has targeted improving the performance of memory hierarchy. In this thesis, we examine the behavior of dynamically allocated data structures (DADS) and programs with irregular access patterns (PIAP). DADS and PIAP use dynamic memory management or algorithms with unpredictable behaviour. By simulating some applications of dynamically allocated data structures (DADS) and programs with irregular access patterns (PIAP), it is found that general cache management policies can not effectively use the treasurable cache resources for DADS and PIAP. We explored the use of mathematical formula applied to signal processing to improve the performance of memory hierarchy.

scholarly journals Memory access behavior of dynamically allocated data structures and programs with irregular access patterns

2021 ◽  
Author(s):  
Zhen Yu
Keyword(s):  
Data Structures ◽  
Memory Management ◽  
Memory Hierarchy ◽  
Research Work ◽  
Memory Access ◽  
Mathematical Formula ◽  
Dynamic Memory ◽  
Dynamic Memory Management ◽  
Management Policies ◽  
Access Patterns

With the development of modern computers, memory latencies have become a key bottleneck for the performance of computer systems. Since then, much research work has targeted improving the performance of memory hierarchy. In this thesis, we examine the behavior of dynamically allocated data structures (DADS) and programs with irregular access patterns (PIAP). DADS and PIAP use dynamic memory management or algorithms with unpredictable behaviour. By simulating some applications of dynamically allocated data structures (DADS) and programs with irregular access patterns (PIAP), it is found that general cache management policies can not effectively use the treasurable cache resources for DADS and PIAP. We explored the use of mathematical formula applied to signal processing to improve the performance of memory hierarchy.

Towards cost-effective and elastic cloud database deployment via memory disaggregation

2021 ◽  
Vol 14 (10) ◽  
pp. 1900-1912
Author(s):  
Yingqiang Zhang ◽  
Chaoyi Ruan ◽  
Cheng Li ◽  
Xinjun Yang ◽  
Wei Cao ◽  
...  
Keyword(s):  
Memory Management ◽  
High Speed ◽  
Relational Databases ◽  
Large Fraction ◽  
Cost Effective ◽  
Data Access ◽  
Remote Memory ◽  
Recent Emergence ◽  
Data Access Patterns ◽  
Access Patterns

It is challenging for cloud-native relational databases to meet the ever-increasing needs of scaling compute and memory resources independently and elastically. The recent emergence of memory disaggregation architecture, relying on high-speed RDMA network, offers opportunities to build cost-effective and elastic cloud-native databases. There exist proposals to let unmodified applications run transparently on disaggregated systems. However, running relational database kernel atop such proposals experiences notable performance degradation and time-consuming failure recovery, offsetting the benefits of disaggregation. To address these challenges, in this paper, we propose a novel database architecture called LegoBase, which explores the co-design of database kernel and memory disaggregation. It pushes the memory management back to the database layer for bypassing the Linux I/O stack and re-using or designing (remote) memory access optimizations with an understanding of data access patterns. LegoBase further splits the conventional ARIES fault tolerance protocol to independently handle the local and remote memory failures for fast recovery of compute instances. We implemented LegoBase atop MySQL. We compare LegoBase against MySQL running on a standalone machine and the state-of-the-art disaggregation proposal Infiniswap. Our evaluation shows that even with a large fraction of data placed on the remote memory, LegoBase's system performance in terms of throughput (up to 9.41% drop) and P99 latency (up to 11.58% increase) is comparable to the monolithic MySQL setup, and significantly outperforms (1.99x-2.33x, respectively) the deployment of MySQL over Infiniswap. Meanwhile, LegoBase introduces an up to 3.87x and 5.48x speedup of the recovery and warm-up time, respectively, over the monolithic MySQL and MySQL over Infiniswap, when handling failures or planned re-configurations.

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