LWMPI: An MPI library for NoC‐based lightweight manycore processors with on‐chip memory constraints

2021 ◽  
Author(s):  
João Fellipe Uller ◽  
João Vicente Souto ◽  
Pedro Henrique Penna ◽  
Márcio Castro ◽  
Henrique Freitas ◽  
...  
Keyword(s):  
Manycore Processors ◽  
On Chip ◽  
Memory Constraints

Wavelength-Reused Hierarchical Optical Network on Chip Architecture for Manycore Processors

2019 ◽  
Vol 4 (2) ◽  
pp. 231-244 ◽  
Author(s):  
Feiyang Liu ◽  
Haibo Zhang ◽  
Yawen Chen ◽  
Zhiyi Huang ◽  
Huaxi Gu
Keyword(s):  
Optical Network ◽  
Network On Chip ◽  
Manycore Processors ◽  
On Chip

scholarly journals Locality-Aware Task Scheduling and Data Distribution for OpenMP Programs on NUMA Systems and Manycore Processors

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Ananya Muddukrishna ◽  
Peter A. Jonsson ◽  
Mats Brorsson
Keyword(s):  
Task Scheduling ◽  
Data Distribution ◽  
Data Access ◽  
Improve Performance ◽  
Manycore Processors ◽  
Cache Access ◽  
On Chip ◽  
Processor Caches ◽  
Architectural Knowledge ◽  
The Impact

Performance degradation due to nonuniform data access latencies has worsened on NUMA systems and can now be felt on-chip in manycore processors. Distributing data across NUMA nodes and manycore processor caches is necessary to reduce the impact of nonuniform latencies. However, techniques for distributing data are error-prone and fragile and require low-level architectural knowledge. Existing task scheduling policies favor quick load-balancing at the expense of locality and ignore NUMA node/manycore cache access latencies while scheduling. Locality-aware scheduling, in conjunction with or as a replacement for existing scheduling, is necessary to minimize NUMA effects and sustain performance. We present a data distribution and locality-aware scheduling technique for task-based OpenMP programs executing on NUMA systems and manycore processors. Our technique relieves the programmer from thinking of NUMA system/manycore processor architecture details by delegating data distribution to the runtime system and uses task data dependence information to guide the scheduling of OpenMP tasks to reduce data stall times. We demonstrate our technique on a four-socket AMD Opteron machine with eight NUMA nodes and on the TILEPro64 processor and identify that data distribution and locality-aware task scheduling improve performance up to 69% for scientific benchmarks compared to default policies and yet provide an architecture-oblivious approach for programmers.

Overcoming the Challenges of Porting OpenCV to TI's Embedded ARM + DSP Platforms

2012 ◽  
Vol 49 (3) ◽  
pp. 260-274 ◽  
Author(s):  
Joseph Coombs ◽  
Rahul Prabhu ◽  
Greg Peake
Keyword(s):  
Digital Signal Processor ◽  
Digital Signal ◽  
Academic Community ◽  
System On Chip ◽  
Floating Point ◽  
Software Packages ◽  
Functional Development ◽  
On Chip ◽  
Algorithm Implementation ◽  
Memory Constraints

The growing performance and decreasing price of embedded processors are opening many doors, for both developers in the industry and in academia. However, the complexities of these systems can create serious developmental bottlenecks. Sophisticated software packages such as OpenCV can assist in both the functional development and educational aspects of these otherwise complex applications; such tools lend themselves very well to use by the academic community, in particular in providing examples of algorithm implementation. However the task of migrating this software to embedded platforms poses its own challenges. This paper will review how to mitigate some of these issues, including C++ implementation, memory constraints, floating-point support, and opportunities to maximise performance using vendor-optimised libraries and integrated accelerators or co-processors. Finally, we will introduce a new effort by Texas Instruments to optimise vision systems by running OpenCV on the C6000™ digital signal processor architecture. Benchmarks will show the advantage of using the DSP by comparing the performance of a DSP+ARM® system-on-chip (SoC) processor against an ARM-only device.

Gut microbiome a promising target for management of respiratory diseases

2020 ◽  
Vol 477 (14) ◽  
pp. 2679-2696
Author(s):  
Riddhi Trivedi ◽  
Kalyani Barve
Keyword(s):  
Respiratory Diseases ◽  
New Technology ◽  
Bacterial Flora ◽  
Systemic Circulation ◽  
The Body ◽  
Lung Pathology ◽  
Promising Target ◽  
Current Therapies ◽  
Intestinal Microbial Flora ◽  
On Chip

The intestinal microbial flora has risen to be one of the important etiological factors in the development of diseases like colorectal cancer, obesity, diabetes, inflammatory bowel disease, anxiety and Parkinson's. The emergence of the association between bacterial flora and lungs led to the discovery of the gut–lung axis. Dysbiosis of several species of colonic bacteria such as Firmicutes and Bacteroidetes and transfer of these bacteria from gut to lungs via lymphatic and systemic circulation are associated with several respiratory diseases such as lung cancer, asthma, tuberculosis, cystic fibrosis, etc. Current therapies for dysbiosis include use of probiotics, prebiotics and synbiotics to restore the balance between various species of beneficial bacteria. Various approaches like nanotechnology and microencapsulation have been explored to increase the permeability and viability of probiotics in the body. The need of the day is comprehensive study of mechanisms behind dysbiosis, translocation of microbiota from gut to lung through various channels and new technology for evaluating treatment to correct this dysbiosis which in turn can be used to manage various respiratory diseases. Microfluidics and organ on chip model are emerging technologies that can satisfy these needs. This review gives an overview of colonic commensals in lung pathology and novel systems that help in alleviating symptoms of lung diseases. We have also hypothesized new models to help in understanding bacterial pathways involved in the gut–lung axis as well as act as a futuristic approach in finding treatment of respiratory diseases caused by dysbiosis.

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