Presentation
SAGA: A Memory-Efficient \underline{A}ccelerator for \underline{GA}NN Construction via Harnessing Vertex \underline{S}imilarity
DescriptionGraph-traversal-based Approximate Nearest Neighbor (GANN) search and construction have become key retrieval techniques in various domains, such as recommendation systems and social networks. However, deploying GANN in real-world scenarios faces significant challenges, as high-dimensional vertices within the graph can lead to intensive memory demands. Although architectures like NDSearch have been proposed to accelerate GANN search, they are hard to deploy for GANN construction, as their pre-processing methods introduce massive overhead in dynamic graphs.
In this paper, given the observation that neighboring vertices in a dynamic graph exhibit feature similarity, we propose SAGA, the first accelerator that alleviates memory bound in GANN construction.
To capture this similarity, we directly leverage the first step of construction to gather vertices with the same starting point into a cluster to minimize the similarity detection overhead.
Next, we decompose vertices into key and non-key ones, where their deltas fall in a narrow range, which is suitable to be quantized to lower bit widths.
Building upon this approach, we design a specialized architecture, which efficiently implements the GANN construction by two-level scheduling and a mixed-precision supported bit-serial unit.
Through comprehensive evaluation, we demonstrate that SAGA can achieve an average speedup of 9.30$\times$, 4.87$\times$, 4.15$\times$ and 35.46$\times$, 7.60$\times$, 5.15$\times$ energy savings over CPU, GPU and NDSearch, respectively, while retaining task accuracy.
In this paper, given the observation that neighboring vertices in a dynamic graph exhibit feature similarity, we propose SAGA, the first accelerator that alleviates memory bound in GANN construction.
To capture this similarity, we directly leverage the first step of construction to gather vertices with the same starting point into a cluster to minimize the similarity detection overhead.
Next, we decompose vertices into key and non-key ones, where their deltas fall in a narrow range, which is suitable to be quantized to lower bit widths.
Building upon this approach, we design a specialized architecture, which efficiently implements the GANN construction by two-level scheduling and a mixed-precision supported bit-serial unit.
Through comprehensive evaluation, we demonstrate that SAGA can achieve an average speedup of 9.30$\times$, 4.87$\times$, 4.15$\times$ and 35.46$\times$, 7.60$\times$, 5.15$\times$ energy savings over CPU, GPU and NDSearch, respectively, while retaining task accuracy.
Event Type
Research Manuscript
TimeWednesday, June 252:45pm - 3:00pm PDT
Location3001, Level 3
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