| Rank |
IPC (public traces) |
Speedup |
Contestant(s) |
Paper | Files | Submitted |
|---|---|---|---|---|---|---|
| André Seznec (IRISA/INRIA) | Exploring Value Prediction with the EVES predictor | .tar.gz | June 2018 | |||
| Yasuo Ishii (Arm) | Context-Base Computational Value Predictor with Value Compression | .tar.gz | June 2018 | |||
| Kenichi Koizumi, Kei Hiraki and Mary Inaba (The University of Tokyo, Japan) | H3VP: History-based High-reliable Hybrid Value Predictor | .tar.gz | June 2018 | |||
| CVP | Baseline | June 2018 |
Perfect Value Prediction achieves 5.755 IPC (+107.0%).
| Rank |
IPC (public traces) |
Speedup |
Contestant(s) |
Paper | Files | Submitted |
|---|---|---|---|---|---|---|
| André Seznec (IRISA/INRIA) | Exploring Value Prediction with the EVES predictor | .tar.gz | June 2018 | |||
| Kenichi Koizumi, Kei Hiraki and Mary Inaba (The University of Tokyo, Japan) | H3VP: History-based High-reliable Hybrid Value Predictor | .tar.gz | June 2018 | |||
| CVP | Baseline | June 2018 |
Perfect Value Prediction achieves 5.755 IPC (+107.0%).
| Rank |
IPC (public traces) |
Speedup |
Contestant(s) |
Paper | Files | Submitted |
|---|---|---|---|---|---|---|
| André Seznec, Kleovoulos Kalaitzidis (IRISA/INRIA) | Exploring Value Prediction Limits | .tar.gz | Feb. 2020 | |||
| Chirag Sakhuja, Anjana Subramanian, Pawanbalakri Joshi, Akanksha Jain and Calvin Lin (The University of Texas at Austin) | Combining Branch History and Value History For Improved Value Prediction | .tar.gz | Nov. 2019 | |||
| Arpit Gupta, Parv Mor, Hritvik Taneja and Biswabandan Panda (Indian Institute of Technology Kanpur) | STEVES: Pushing the Limits of Value Predictors with Sliding FCM and EVES | .tar.gz | May 2019 | |||
| André Seznec (IRISA/INRIA) | Exploring Value Prediction with the EVES predictor | .tar.gz | June 2018 | |||
| Nayan Deshmukh, Snehil Verma, Prakhar Agrawal, Biswabandan Panda, Mainak Chaudhuri (Indian Institute of Technology Kanpur) | DFCM++: Augmenting DFCM with Early Update and Data Dependency-driven Value Estimation | .tar.gz | June 2018 | |||
| Kenichi Koizumi, Kei Hiraki and Mary Inaba (The University of Tokyo, Japan) | H3VP: History-based High-reliable Hybrid Value Predictor | .tar.gz | June 2018 | |||
| CVP | Baseline | June 2018 |
Perfect Value Prediction achieves 5.755 IPC (+107.0%). Predictor provided in the kit achieves 3.050 IPC (+9.7%).
VP_ENABLE = 1
VP_PERFECT = 0
WINDOW_SIZE = 256
FETCH_WIDTH = 16
FETCH_NUM_BRANCH = 0
FETCH_STOP_AT_INDIRECT = 0
FETCH_STOP_AT_TAKEN = 0
FETCH_MODEL_ICACHE = 0
PERFECT_BRANCH_PRED = 0
PERFECT_INDIRECT_PRED = 0
PIPELINE_FILL_LATENCY = 5
NUM_LDST_LANES = 0 (unbounded)
NUM_ALU_LANES = 0 (unbounded)
MEMORY HIERARCHY CONFIGURATION---------------------
PERFECT_CACHE = 0
WRITE_ALLOCATE = 1
Within-pipeline factors:
AGEN latency = 1 cycle
Store Queue (SQ): SQ size = window size, oracle memory disambiguation, store-load forwarding = 1 cycle after store's or load's agen.
* Note: A store searches the L1$ at commit. The store is released
* from the SQ and window, whether it hits or misses. Store misses
* are buffered until the block is allocated and the store is
* performed in the L1$. While buffered, conflicting loads get
* the store's data as they would from the SQ.
L1$: 32 KB, 4-way set-assoc., 64B block size, 2-cycle search latency
L2$: 1 MB, 8-way set-assoc., 64B block size, 12-cycle search latency
L3$: 8 MB, 16-way set-assoc., 128B block size, 60-cycle search latency
Main Memory: 150-cycle fixed search time