The CANDLE benchmark codes implement deep learning architectures relevant to problems in cancer. These architectures address problems at different biological scales, specifically problems at the molecular, cellular and population scales.
To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark ecp-candle.
* Uploading of benchmark result data to OpenBenchmarking.org is always optional (opt-in) via the Phoronix Test Suite for users wishing to share their results publicly. ** Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform. *** Test profile page view reporting began March 2021. Data updated weekly as of 21 April 2024.
Revision History
pts/ecp-candle-1.1.0 [View Source] Mon, 02 Aug 2021 15:13:15 GMT Update against ECP-CANDLE Benchmarks v0.4 upstream.
pts/ecp-candle-1.0.1 [View Source] Sun, 23 Aug 2020 14:20:46 GMT Drop P2B1 option as it seems borked for some systems.
pts/ecp-candle-1.0.0 [View Source] Wed, 12 Aug 2020 18:15:02 GMT Initial commit of CANDLE cancer deep learning benchmark.
OpenBenchmarking.org metrics for this test profile configuration based on 278 public results since 12 August 2020 with the latest data as of 3 May 2021.
Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.
Based on OpenBenchmarking.org data, the selected test / test configuration (ECP-CANDLE 0.3 - Benchmark: P3B1) has an average run-time of 16 minutes. By default this test profile is set to run at least 1 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.
Does It Scale Well With Increasing Cores?
Yes, based on the automated analysis of the collected public benchmark data, this test / test settings does generally scale well with increasing CPU core counts. Data based on publicly available results for this test / test settings, separated by vendor, result divided by the reference CPU clock speed, grouped by matching physical CPU core count, and normalized against the smallest core count tested from each vendor for each CPU having a sufficient number of test samples and statistically significant data.
Tested CPU Architectures
This benchmark has been successfully tested on the below mentioned architectures. The CPU architectures listed is where successful OpenBenchmarking.org result uploads occurred, namely for helping to determine if a given test is compatible with various alternative CPU architectures.