Name
Title | ||
|---|---|---|
Single-node partitioned-memory for huge graph analytics: cost and performance trade-offs |
Abstract | ||
|---|---|---|
ABSTRACTBecause of cost, non-volatile memory NVDIMMs such as Intel Optane are attractive in single-node big-memory systems. We evaluate performance and cost trade-offs when using Optane as volatile memory for huge-graph analytics. We study two scalable graph applications with different work locality, access patterns, and parallelism. We evaluate single and partitioned address spaces---Memory and AppDirect modes---and compare with distributed executions on GPU-accelerated and CPU-based supercomputers. We show that AppDirect can perform and scale better than Memory for the largest working sets (12%), even when dominated by irregular access patterns, if most accesses are NUMA-local and Optane accesses are frequently reads. Surprisingly, between Memory and AppDirect, processor-cache performance can change due to line invalidations; updates to the caching policy (via non-temporal hints) can make a 25% improvement. We observe that single-node graph analytics frequently has >4--10× cost/performance advantages over distributed-memory executions on supercomputers. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3458817.3476156 | The International Conference for High Performance Computing, Networking, Storage, and Analysis |
Keywords | DocType | ISSN |
non-volatile memory,graph analytics,performance evaluation | Conference | 2167-4329 |
ISBN | Citations | PageRank |
978-1-6654-8390-2 | 0 | 0.34 |
References | Authors | |
34 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sayan Ghosh | 1 | 237 | 16.12 |
| Nathan R. Tallent | 2 | 335 | 25.06 |
| Marco Minutoli | 3 | 0 | 0.34 |
| Mahantesh Halappanavar | 4 | 218 | 33.64 |
| Ramesh Peri | 5 | 0 | 0.34 |
| Kalyanaraman, Ananth | 6 | 221 | 31.95 |