docs: Update benchmark results (#4300)

Author: andygrove

README.md

@@ -40,17 +40,17 @@ Apache DataFusion Comet is a high-performance accelerator for Apache Spark, buil
 performance of Apache Spark workloads while leveraging commodity hardware and seamlessly integrating with the
 Spark ecosystem without requiring any code changes.
 
-**Comet provides a 2x speedup for TPC-H @ 1TB, resulting in 50% cost savings.**
+**Comet provides a ~2x speedup for TPC-DS @ SF 1000 (1TB), resulting in ~50% cost savings.**
 
 That 2x speedup gives you a choice: finish the same Spark workload in half the time on the cluster you already have,
 or match your current Spark performance on roughly half the resources. Either way, the gain translates directly into
 lower cloud bills, reduced on-prem capacity, and lower energy usage, with no changes to your existing Spark SQL,
 DataFrame, or PySpark code. Comet runs on commodity hardware: no GPUs, FPGAs, or other specialized accelerators are
 required, so the savings come from better utilization of the infrastructure you already run on.
 
-![](docs/source/_static/images/benchmark-results/0.15.0/tpch_allqueries.png)
+![](docs/source/_static/images/benchmark-results/0.16.0/tpcds_allqueries.png)
 
-![](docs/source/_static/images/benchmark-results/0.15.0/tpch_queries_compare.png)
+![](docs/source/_static/images/benchmark-results/0.16.0/tpcds_queries_speedup_rel.png)
 
 See the [Comet Benchmarking Guide](https://datafusion.apache.org/comet/contributor-guide/benchmarking.html) for more details.
 
@@ -81,7 +81,7 @@ benefits of Comet's acceleration capabilities without disrupting your Spark appl
 
 ## Getting Started
 
-Comet supports Apache Spark 3.4, 3.5, and 4.0, and provides experimental support for Spark 4.1 and 4.2. See the
+Comet supports Apache Spark 3.4, 3.5, 4.0, and 4.1, and provides experimental support for Spark 4.2. See the
 [installation guide](https://datafusion.apache.org/comet/user-guide/installation.html) for the detailed
 version, Java, and Scala compatibility matrix.
 

docs/source/_static/images/benchmark-results/0.16.0/tpch_allqueries_with_tuned.png

@@ -24,7 +24,7 @@ between them. This document is likely biased because the Comet community maintai
 
 We recommend trying out both Comet and Gluten to see which is the best fit for your needs.
 
-This document is based on Comet 0.15.0 and Gluten 1.6.0.
+This document is based on Comet 0.16.0 and Gluten 1.6.0.
 
 ## Architecture
 

docs/source/about/gluten_comparison.md

@@ -25,7 +25,7 @@ The following benchmarks were performed on an EKS cluster (`r6i.24xlarge` instan
 
 Total time to run all queries (lower is better).
 
-![](../../_static/images/benchmark-results/0.15.0/tpch_allqueries_with_tuned.png)
+![](../../_static/images/benchmark-results/0.16.0/tpch_allqueries_with_tuned.png)
 
 The following charts are based on the tuned run using hash join.
 

docs/source/contributor-guide/benchmark-results/tpc-h.md

@@ -40,14 +40,14 @@ Comet also accelerates Apache Iceberg, when performing Parquet scans from Spark.
 
 Comet delivers a performance speedup for many queries, enabling faster data processing and shorter time-to-insights.
 
-The following charts demonstrate Comet accelerating TPC-H @ 1 TB. See the [Comet Benchmarking Guide](https://datafusion.apache.org/comet/contributor-guide/benchmarking.html)
+The following charts demonstrate Comet accelerating TPC-DS @ 1 TB. See the [Comet Benchmarking Guide](https://datafusion.apache.org/comet/contributor-guide/benchmarking.html)
 for details.
 
-![](_static/images/benchmark-results/0.15.0/tpch_allqueries.png)
+![](_static/images/benchmark-results/0.16.0/tpcds_allqueries.png)
 
-Here is a breakdown showing relative performance of Spark and Comet for each TPC-H query.
+Here is a breakdown showing relative speedup for each TPC-DS query.
 
-![](_static/images/benchmark-results/0.15.0/tpch_queries_compare.png)
+![](_static/images/benchmark-results/0.16.0/tpcds_queries_speedup_rel.png)
 
 ## Use Commodity Hardware