Markdown rendering for the toplevel README.md to improve readability on github

README → README.md

-ONLINE DOCUMENTATION
+# Spark
 
-You can find the latest Spark documentation, including a programming guide,
-on the project wiki at http://github.com/mesos/spark/wiki. This file only
-contains basic setup instructions.
+Lightning-Fast Cluster Computing - <http://www.spark-project.org/>
 
 
+## Online Documentation
 
-BUILDING
+You can find the latest Spark documentation, including a programming
+guide, on the project wiki at <http://github.com/mesos/spark/wiki>. This
+file only contains basic setup instructions.
+
+
+## Building
 
 Spark requires Scala 2.8. This version has been tested with 2.8.1.final.
+Experimental support for Scala 2.9 is available in the `scala-2.9` branch.
 
 The project is built using Simple Build Tool (SBT), which is packaged with it.
-To build Spark and its example programs, run sbt/sbt update compile.
+To build Spark and its example programs, run:
+
+    sbt/sbt update compile
 
 To run Spark, you will need to have Scala's bin in your $PATH, or you
-will need to set the SCALA_HOME environment variable to point to where
+will need to set the `SCALA_HOME` environment variable to point to where
 you've installed Scala. Scala must be accessible through one of these
 methods on Mesos slave nodes as well as on the master.
 
-To run one of the examples, use ./run <class> <params>. For example,
-./run spark.examples.SparkLR will run the Logistic Regression example.
+To run one of the examples, use `./run <class> <params>`. For example:
+
+    ./run spark.examples.SparkLR local[2]
+
+will run the Logistic Regression example locally on 2 CPUs.
+
 Each of the example programs prints usage help if no params are given.
 
-All of the Spark samples take a <host> parameter that is the Mesos master
+All of the Spark samples take a `<host>` parameter that is the Mesos master
 to connect to. This can be a Mesos URL, or "local" to run locally with one
 thread, or "local[N]" to run locally with N threads.
 
 
+## Configuration
 
-CONFIGURATION
-
-Spark can be configured through two files: conf/java-opts and conf/spark-env.sh.
+Spark can be configured through two files: `conf/java-opts` and
+`conf/spark-env.sh`.
 
-In java-opts, you can add flags to be passed to the JVM when running Spark.
+In `java-opts`, you can add flags to be passed to the JVM when running Spark.
 
-In spark-env.sh, you can set any environment variables you wish to be available
-when running Spark programs, such as PATH, SCALA_HOME, etc. There are also
+In `spark-env.sh`, you can set any environment variables you wish to be available
+when running Spark programs, such as `PATH`, `SCALA_HOME`, etc. There are also
 several Spark-specific variables you can set:
-- SPARK_CLASSPATH: Extra entries to be added to the classpath, separated by ":".
-- SPARK_MEM: Memory for Spark to use, in the format used by java's -Xmx option
-             (for example, 200m meams 200 MB, 1g means 1 GB, etc).
-- SPARK_LIBRARY_PATH: Extra entries to add to java.library.path for locating
-                      shared libraries.
-- SPARK_JAVA_OPTS: Extra options to pass to JVM.
-
-Note that spark-env.sh must be a shell script (it must be executable and start
-with a #! header to specify the shell to use).
+- `SPARK_CLASSPATH`: Extra entries to be added to the classpath, separated by ":".
+- `SPARK_MEM`: Memory for Spark to use, in the format used by java's `-Xmx`
+  option (for example, `-Xmx200m` meams 200 MB, `-Xmx1g` means 1 GB, etc).
+- `SPARK_LIBRARY_PATH`: Extra entries to add to `java.library.path` for locating
+  shared libraries.
+- `SPARK_JAVA_OPTS`: Extra options to pass to JVM.
+
+Note that `spark-env.sh` must be a shell script (it must be executable and start
+with a `#!` header to specify the shell to use).