# Domain-Specific Languages for Algorithmic Graph Processing: A Systematic Literature Review
## 3 Results
### Result Synthesis
#### 3.2.4 RQ4. Target Platforms, Scalability, and Computational Performance
These graphs usually have a Poisson-like degree distribution and lack the clustering or community structures seen in real-world networks. Typically, an experimental evaluation of a given DSL's performance will use more than one input graph, varying in size, to assess how scaling up affects performance. Graph topology and the nature of the graph algorithm are also factors with an impact on running times. For example, [PERSON] et al. [29] provide performance numbers for GraphIt on up to seven graphs and seven graph algorithms. The input graphs include social networks, road networks, and the Netflix rating dataset. The GraphIt implementation of PageRank outperforms state-of-the-art solutions on four out of five graphs, but it is outperformed by Gazelle on the road network, where the latter benefits from Vector-Sparse vectorization, which works well on graphs with low degrees. Because of this, it may be challenging to assess which solution performs best in the absolute: competitive analyses must consider the same algorithms and the same input graphs. Moreover, surveyed DSLs target different execution platforms with different data representations (graph, adjacency matrix, relational data), target languages, computing paradigms, and hardware architectures. Even for two DSLs targeting the same platform, running times must be obtained on the same machine to allow a meaningful and reliable comparison. These benchmarking difficulties have \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**DSL**} & \multirow{2}{*}{**Target Platform**} & \multicolumn{6}{c}{**Graph Types Used**} & \multicolumn{6}{c}{**Largest Graph Used in Evaluation**} \\ \cline{3-10}  & & & **Social Network** & **Web Graph** & **R-MAT** & **Road Network** & **Random Graph** & **Graph Type** & **\#N** & **\#E** \\ \hline \multirow{2}{*}{Elixir} & single-machine & & & ✓ & ✓ & ✓ & random graph & 8.3 M & 33.5 M \\ \cline{2-10}  & single-machine & ✓ & ✓ & & & & & social network & 41.6 M & 1.4 B \\ \cline{2-10}  & distributed & ✓ & ✓ & & & & ✓ & web graph & 77.7 M & 2.9 B \\ \hline \multirow{2}{*}{Falcon} & single-machine & & & ✓ & ✓ & ✓ & ✓ & R-MAT & 80 M & 256 M \\ \cline{2-10}  & distributed & ✓ & ✓ & ✓ & & & & R-MAT & 1.2 B & 12 B \\ \hline \multirow{2}{*}{Graphit} & single-machine & ✓ & ✓ & & ✓ & & & social network & 65.6 M & 3.6 B \\ \cline{2-10}  & StarPlat & single-machine & ✓ & ✓ & ✓ & ✓ & ✓ & social network & 21.2 M & 262 M \\ \hline PyGB & linear algebra & & & & & & ✓ & & 2048 & 92682 \\ \hline \multirow{2}{*}{[33]} & linear algebra & ✓ & & ✓ & ✓ & & & social network & 3.9 M & 69.3 M \\ \hline \multirow{2}{*}{Palgol} & \multirow{2}{*}{distributed} & ✓ & ✓ & & ✓ & ✓ & social network & 59.2 M & 185 M \\ \cline{2-10}  & & & & & & ✓ & & random graph & 10 M & 80 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Scalability overview of surveyed DSLs. been raised in existing surveys and related work [78; 79]. That being said, some articles provide their own performance comparison against state-of-the-art DSLs. GraphIt reports superior performance to Green-Marl's shared-memory back end on different input graphs (between a web graph, a road network, and social networks) for five graph algorithms, namely PageRank, connected components, Bellmann-Ford, SSSP, and betweenness centrality. Falcon's collection-based \(\Delta\)-stepping optimization [80] allows it to outperform Green-Marl on road networks. StarPlat also compares its performance against Green-Marl and other non-DSL-based solutions, and it finds that Green-Marl achieves the fastest times on most inputs.