model stringclasses 1
value | dataset stringclasses 3
values | num_tables int64 3 26 | tables listlengths 3 26 | num_batches int64 182 299k | batch_size int64 128 128 | mode stringclasses 1
value | keys_per_page int64 16 16 | total_accesses int64 606k 221M |
|---|---|---|---|---|---|---|---|---|
DLRM | criteo_kaggle_large | 26 | [
{
"id": 0,
"num_rows": 1104,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 163,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 222301,
"embedding_dim": 64
},
{
"id": 3,
"num_rows": 112811,
"embedding_dim": 64
},
{
"id": 4,
"num_rows": 244,
"... | 182 | 128 | inference | 16 | 605,696 |
DLRM | avazu | 21 | [
{
"id": 0,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 4737,
"embedding_dim": 64
},
{
"id": 3,
"num_rows": 7745,
"embedding_dim": 64
},
{
"id": 4,
"num_rows": 26,
"embedding_... | 82,299 | 128 | inference | 16 | 221,219,712 |
DLRM | avazu | 21 | [
{
"id": 0,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 4737,
"embedding_dim": 64
},
{
"id": 3,
"num_rows": 7745,
"embedding_dim": 64
},
{
"id": 4,
"num_rows": 26,
"embedding_... | 66,501 | 128 | inference | 16 | 178,754,688 |
DLRM | avazu | 21 | [
{
"id": 0,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 7,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 4737,
"embedding_dim": 64
},
{
"id": 3,
"num_rows": 7745,
"embedding_dim": 64
},
{
"id": 4,
"num_rows": 26,
"embedding_... | 62,902 | 128 | inference | 16 | 169,080,576 |
DLRM | taobao | 3 | [
{
"id": 0,
"num_rows": 987991,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 4161138,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 9437,
"embedding_dim": 64
}
] | 239,140 | 128 | inference | 16 | 91,829,760 |
DLRM | taobao | 3 | [
{
"id": 0,
"num_rows": 987991,
"embedding_dim": 64
},
{
"id": 1,
"num_rows": 4161138,
"embedding_dim": 64
},
{
"id": 2,
"num_rows": 9437,
"embedding_dim": 64
}
] | 299,214 | 128 | inference | 16 | 114,898,176 |
CXL-SSD Archetype Routing — Generated Traces
Stage-00 snapshot (2026-05-02) of processed access traces used in the CXL-SSD page-oriented embedding lookup paper.
These are MQSim/MaxEmbed-format traces derived from public recommendation datasets (MovieLens, Criteo, Avazu, Taobao, Amazon, Yelp, …) and the Qwen KV-Cache traces. They feed into the Cylon (FEMU) and MQSim simulators.
Companion repos
| Component | URL |
|---|---|
| Outer paper repo | https://github.com/shadowcollecter/cxlssd-archetype-routing |
| Cylon-full (FEMU CXL-SSD) | https://github.com/shadowcollecter/cylon-full-paper |
| MQSim-CXL fork | https://github.com/shadowcollecter/mqsim-cxl-paper |
| MaxEmbed | https://github.com/shadowcollecter/maxembed-paper |
Layout
Each subdirectory corresponds to one source dataset and contains traces
of the form <layout>_<ratio>.trace plus the matching MQSim
workload.xml / config snippets.
The trace ASCII format (per MQSim convention):
<arrival_time_ns> <device_num=0> <LSN_byte_addr> <size_sectors=8> <type 0=W|1=R>
Page-oriented variant: LSN is the byte address of a 4 KB page; size = 8 sectors (4 KB).
Provenance
Generated from /research_data/raw/<dataset>/ via the pipelines in
MERCI_page_aware/analysis/, trace_converter/, and
MaxEmbed/scripts/. See MANIFEST.md for per-dataset preprocessing
parameters.
License
Trace files derived from each upstream dataset inherit the upstream licence. This collection (the bundle layout + associated metadata) is released CC-BY-NC-4.0 for academic reproducibility.
Do not redistribute Criteo/Avazu/Taobao/Amazon raw fields outside academic context.
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