The two figures highlight the strong correlation between Vcpu and the cache hit rate. The time to process brought by the hybrid system.
As Figure 9 shows, GPU processing is the bottleneck for the same number of edges, resulting in a much more for both of these strategies and the gain brought by the cache friendly workload. This leads to a significant hybrid system is proportional to the part of the graph improvement in the CPU processing rate; as a result, the processed concurrently on the CPU. Note that, for both performance of the hybrid system is 2x faster than the other strategies, the performance improves up to a point where the two partitioning strategies.
This performance for both strategies peaks exactly when the demonstrates that although the GPU has limited memory, it graph is equally partitioned. This is because In the case of LOW partitioning strategy, the resulting the GPU is able to efficiently handle the sparser part of the large GPU partition is vastly denser than those of the other graph as it relies on massive multi-threading rather than two strategies.
A denser graph leads to better locality, which caches to hide memory access latency. Hence, the GPU processing Same exploration for a smaller graph.
A smaller graph creates two different implications: it enables offloading a larger partition to the GPU in this case, the graph can fit entirely in the GPU memory ; and, for BFS, a graph with a small number of vertices improves cache hit rate of the algorithm in this case, the bit-vector size is 4MB, and fits entirely in the 12MB of the LLC. In the right end of the figure, where the CPU partition is larger than the GPU partition, CPU processing is the bottleneck and the performance of the three strategies exhibits the same behavior.
Thus, the performance of the hybrid system is proportional to the share of the graph offloaded for processing on the GPU. Note that, because the graph is small and already fits the cache, the beneficial effect of the HIGH partitioning strategy is marginal compared to its Figure 9: Breakdown of execution time for scale graph. In the case where the most of the graph is processed on the GPU the left end of GPU computation is the bottleneck, the GPU bar is as tall Figure 8 , Figure 9 shows the breakdown of execution time for as the computation bar.
Due to the small number of vertices assigned to the two strategies for the same percentage of offloaded edges. The previous two subsections provide degree vertices on the CPU allows processing them faster as explanations for the observed performance due to the graph the CPU has cores clocked higher than the GPU ones.
For partitioning strategies. Moreover, the results confirm that the right end of the figure, the CPU is the bottleneck, and the adding a GPU to a single CPU socket system is indeed a performance improvement brought by the hybrid system is good opportunity to improve the efficiency of graph only proportional to the share of the graph offloaded.
The partitioning strategy that delivers the best performance is behavior of the system is similar to that observed in the different. In general, the rule for choosing a partitioning above discussed smaller graph. This happens because PageRank offers the best performance as it allows the bottleneck requires a larger per-vertex state than BFS; hence, the processor, the CPU, to process its partition faster by number of vertices assigned to a partition has a larger effect creating a more cache-friendly partition.
GPU, offering the best processing rate for this scenario. The Effect of Vertex Degree Distribution structures; hence the cache has a lower effect on processing As discussed previously, most real-world graphs obey a performance on the host. The fact that these graphs Figure 10 shows the processing rate in edges per second have skewed vertex degree distribution i guided our choice EPS 3 for a scale RMAT graph. Similar to BFS, the of partitioning strategies, and ii facilitated the aggregation performance increases until the workload is balanced optimization, which aims to reduce the communication between the two processors.
Figure BFS traversal rate for a uniform scale 25 graph. The model generates configurations and different R-MAT graph sizes scale edges with equal probability of setting an edge between any to When GPUs are used the graph is partitioned to obtain best performance. Figure 12 shows BFS traversal rate for a uniform scale reducing the computation time, as the right figure shows.
How does the hybrid system scale with superior performance compared to the CPU. We believe that these are Figure 12 highlights that, when the graph has a uniform important questions because systems with two processing degree distribution the hybrid system performs almost the components a CPU socket and a GPU or one with few same irrespective of the partitioning strategy as all strategies GPUs can become common-place due to their cost produce partitions with similar characteristics.
Because the effectiveness. Compared to processing the whole graph on the cores and 2 GPUs and graph scales scale to GPU, the hybrid system performance is inferior even when First, we focus on the analysis of configurations with two the majority of the graph is placed on the GPU the left side processing units.
The figure shows that for all graph sizes, of the figure. This is because the benefit from concurrently the hybrid 1S1G system performs faster than the dual- processing part of the graph on the CPU is masked by the socket system 2S. Adding a second socket doubles the communication overhead, which is more significant than for amount of last level cache, a critical resource for BFS the R-MAT graphs.
However, the performance of 1S1G, brought scale uniform graph. Unlike the R-MAT workload and by matching the heterogeneous graph workload with the similar to the performance of the smaller graph above, all hybrid system, outperforms that of the dual-socket partitioning strategies perform similarly.
Moreover, there is symmetric system: between 1. Agrawal et al. The abundance of LLC aggregated by the four socket system enables it to scale well with increased graph size.
Still, the figure shows that a hybrid 1S1G system offers competitive performance to that of the four socket system 4S at a lower Figure BFS performance on a scale uniform graph. Left: traversal rate. Interestingly, adding a GPU sharply improves the performance. A hybrid 1S1G configuration achieves speedups between 1. The benefit of offloading part of the workload to GPUs is confirmed again when adding a second a GPU where the plot shows another jump in performance for all workloads.
Note that because PageRank requires more state runtime is far from a straightforward task. Parallel per-vertex compared to BFS, we were not able to process a implementations require substantial effort to maximize the scale graph like we did for BFS.
In fact, 1S1G offers There is no shortage of work on graph partitioning for 1. Traditionally, the problem is defined as smallest two graphs.
It has been shown that this problem is NP-hard harness extra processing elements, achieving up to 1. We believe that classical solutions do as 1. It is worth pointing out that such performance is on hybrid platforms. Some heuristics such as Kernighan—Lin competitive, yet at a lower cost, with the performance results [21], has quadratic O n2logn time complexity, which is of the latest Graph list published as of writing this paper prohibitively expensive for the scale of the graphs we target.
June for graphs of the same size. In this case, partitioning is done by first Graph In general, the raw performance is much lower than using one of the expensive techniques mentioned above. Moreover, original graph.
Moreover, they target symmetric This doubles the memory footprint of the graph, and parallel platforms as they focus on producing balanced significantly affects the percentage of edges that can be partitions, which is not sufficient for a hybrid system that has offloaded to the GPU, hence the raw performance of the two processing units with largely different characteristics.
For example, several studies focus similar performance to a 2S dual-socket one. One second GPU increases the percentage of total offloaded important recent effort by Chhugani et al. This highlights the vital importance of eliminate the overhead of atomic operations by using having more memory on the GPU for large graphs. We believe that their other hit rate for algorithms that use summary data structures, techniques are complementary to our approach: they can be and better balances the load across the cores for ones that applied to the BFS kernel that runs on the CPU to improve do not use them.
If the graph is small, most of the graph can fit in the Finally, past projects have explored GPU-only graph GPU, and there is space to better balance the load processing.
However, these projects either assume that the between the two processing units. In both cases, due to the limited of the graph. The policy for which partitioning strategy to memory space available on the GPU, the scale of the graphs use in this case is as follows.
If the algorithm employs that can be processed is significantly smaller than the graphs summary data structures, placing the low degree vertices presented in this paper.
We stress that these guidelines solutions that reduce the communication overheads? We show that, in the case of scale-free graphs, the with a larger set of algorithms, graphs topologies, and GPU communication overhead can be significantly reduced — to models.
We phrase these guidelines as answers to a number the point that it becomes negligible relative to the of questions. Aggregation works well for four reasons. First, real-world graphs have skewed connectivity distribution. Second, the A: Yes, for scale-free graphs. One concern when number of partitions the graph is split into is relatively considering using a hybrid system is the limited GPU low only two for a hybrid system with one GPU.
Third, memory that may render using a GPU ineffective when aggregation can be applied to many practical graph processing large graphs. We show, however, that it is algorithms, such as BFS, PageRank and Single-source possible to offload only a small portion of the graph to the Shortest Path. Fourth, there is practically no cost for GPU and obtain benefits that are higher than the aggregation: conceptually, aggregation moves the proportion of the graph offloaded for GPU processing due computation to where the data is, which must happen to the heterogeneity of the graph workload.
For instance, anyway. A wide-range of graph algorithms can be hybrid system offers a 2x speedup on a scale RMAT implemented on top of TOTEM, which exposes similar graph compared to a dual-socket system see Figure Our A: Yes, the low-cost partitioning strategies we explore — experiments show that being generic — that is, being able all informed by vertex connectivity — provide in all cases to support multiple algorithms and not only the popular better performance than blind, random partitioning.
The answer is nuanced and the choice of when increasing the number of processing elements. If the socket and dual-GPU is capable of 1. Thus, though at the bottom of recent entries in the Graph the goal of partitioning is to improve the CPU list. Scalable Graph Exploration on Multicore Processors. SuperComputing Nov. Analysis of topological [24] P. On the Evolution of Random Graphs. Physical review. Statistical, nonlinear, and soft matter physics.
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Accelerating CUDA graph algorithms at maximum warp. PACT Oct. A network analysis of the Italian overnight money market. Journal of Economic Dynamics and Control. Lethality and centrality in protein networks. All-pairs shortest-paths for large graphs on the GPU.
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