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# System prepended metadata

title: Untitled

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## Paper Writing
## Introduction
## Background
1. Graph Computing (Where paralleism is coming in graph from) CSR potential problem 
-- Discuss fundamentals of graph and its application algorithm like page rank, single source shortest path and etc briefly
--Highlight non-amortization of memory access time in graph computation
3. GPU Architecture (Describe stream multiprocessor in detail)
4. GPU Execution Model
5. Microarchitectural Limitation of GPU for Graph Processing Load
   --Highlighting memory coalescing, uncoalescing and load imbalance. 
   --Enlist inefficiencies of vertex-centric graph parallel computing in GPU
   --Underutilization of SIMD (Load Imbalance and branch divergence issues)
   --Discuss "Scalarized vertex-centric parallel graph computing" method from "Graph waving architecture paper" in detail
7. Enlist few bottlenecks in performance imporvement
    -- Amdhal's law (Law of diminishing return): Illustrate it in current context of graph computation on GPU. And explain upper performance improvement limit from proposed method 
## Related Work (Literature Survey)
1. Discuss utilization of SIMD unit reported by other authors for Graph computation 

## Charaacterization section
## Proposed Architecture
## Table , PTS assembly code, 
DID library