Table of Contents
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What versions of Apache Spark does the RAPIDS Accelerator for Apache Spark support?
How can I check if the RAPIDS Accelerator is installed and which version is running?
How long does it take to translate a query to run on the GPU?
How can I tell what will run on the GPU and what will not run on it?
Why does the plan for the GPU query look different from the CPU query?
How does the Spark scheduler decide what to do on the GPU vs the CPU?
Why does my query show as not on the GPU when Adaptive Query Execution is enabled?
Does the RAPIDS Shuffle Manager support External Shuffle Service (ESS)?
Is the GPU needed on the driver? Are there any benefits to having a GPU on the driver?
How many tasks can I run per executor? How many should I run per executor?
spark.executor.coresspark.task.resource.gpu.amountspark.rapids.sql.concurrentGpuTasksWhy am I getting a buffer overflow error when using the KryoSerializer?
Why am I getting “Unable to acquire buffer” or “Trying to free an invalid buffer”?
Please see Software Requirements section for complete list of Apache Spark versions supported by RAPIDS Accelerator. The plugin replaces parts of the physical plan that Apache Spark considers internal. The code for these plans can change, even between bug fix releases. As a part of our process, we try to stay on top of these changes and release updates as quickly as possible.
The RAPIDS Accelerator for Apache Spark officially supports:
Most distributions based on a supported Apache Spark version should work, but because the plugin replaces parts of the physical plan that Apache Spark considers to be internal the code for those plans can change from one distribution to another. We are working with most cloud service providers to set up testing and validation on their distributions.
Please look here for the list of supported CUDA versions.
Please see Hardware Requirements section for the list of GPUs that the RAPIDS Accelerator has been tested on. It is possible to run the plugin on GeForce desktop hardware with Volta or better architectures. GeForce hardware does not support CUDA forward compatibility, and will need CUDA 11.5 installed. If not, the following error will be displayed:
ai.rapids.cudf.CudaException: forward compatibility was attempted on non supported HW
at ai.rapids.cudf.Cuda.getDeviceCount(Native Method)
at com.nvidia.spark.rapids.GpuDeviceManager$.findGpuAndAcquire(GpuDeviceManager.scala:78)
More information about cards that support forward compatibility can be found here.
On startup the RAPIDS Accelerator will log a warning message on the Spark driver showing the version with a message that looks something like this:
WARN RapidsPluginUtils: RAPIDS Accelerator 22.10.0 using cudf 22.10.0.
The full RAPIDS Accelerator, RAPIDS Accelerator JNI and cudf build properties are logged at INFO level in the Spark driver and executor logs with messages that are similar to the following:
INFO RapidsPluginUtils: RAPIDS Accelerator build: {version=22.10.0-SNAPSHOT, user=, url=https://github.com/NVIDIA/spark-rapids.git, date=2022-09-02T12:41:30Z, revision=66450a3549d7cbb23799ec7be2f6f02b253efb85, cudf_version=22.10.0-SNAPSHOT, branch=HEAD}
INFO RapidsPluginUtils: RAPIDS Accelerator JNI build: {version=22.10.0-SNAPSHOT, user=, url=https://github.com/NVIDIA/spark-rapids-jni.git, date=2022-09-02T03:35:21Z, revision=76b71b9ffa1fa4237365b51485d11362cbfb99e5, branch=HEAD}
INFO RapidsPluginUtils: cudf build: {version=22.10.0-SNAPSHOT, user=, url=https://github.com/rapidsai/cudf.git, date=2022-09-02T03:35:21Z, revision=c273da4d6285d6b6f9640585cb3b8cf11310bef6, branch=HEAD}
Currently a limited set of SQL and DataFrame operations are supported, please see the configs and supported operations for a more complete list of what is supported. Some of the MLlib functions, such as PCA are supported. Some of structured streaming is likely to be accelerated, but it has not been an area of focus right now. Other areas like GraphX or RDDs are not accelerated.
The RAPIDS Accelerator supports the DataFrame API which is implemented in Spark as Dataset[Row]. If you are using Dataset[Row] that is equivalent to the DataFrame API. In either case the operations that are supported for acceleration on the GPU are limited. For example using custom classes or types with Dataset are not supported. Neither are using APIs that take Row as an input, or ones that take Scala or Java functions to operate. This includes operators like flatMap, foreach, or foreachPartition. Such queries will still execute correctly when using the RAPIDS Accelerator, but it is likely most query operations will not be performed on the GPU.
With custom types the Dataset API generates query plans that use opaque lambda expressions to access the custom types. The opaque expressions prevent the RAPIDS Accelerator from translating any operation with these opaque expressions to the GPU, since the RAPIDS Accelerator cannot determine how the expression operates.
Please look at the github repository https://github.com/nvidia/spark-rapids. It contains issue tracking and planning for sprints and releases.
Any single operator isn’t a fixed amount faster. So there is no simple algorithm to see how much faster a query will run. In addition, Apache Spark can store intermediate data to disk and send it across the network, both of which we typically see as bottlenecks in real world queries. Generally for complicated queries where all the processing can run on the GPU we see between 3x and 7x speedup, with a 4x speedup typical. We have seen as high as 100x in some specific cases.
Group by operations with high cardinality
Joins with a high cardinality
Sorts with a high cardinality
Window operations, especially for large windows
Complicated processing
Writing Parquet/ORC
Reading CSV
Transcoding (reading an input file and doing minimal processing before writing it out again, possibly in a different format, like CSV to Parquet)
From our tests the GPU typically takes about 2 to 3 seconds to initialize when an executor first starts. If you are only going to run a single query that only takes a few seconds to run this can be problematic. In general if you are going to do 30 seconds or more of processing within a single session the overhead can be amortized.
The time it takes to translate the Apache Spark physical plan to one that can run on the GPU is proportional to the size of the plan. But, it also depends on the CPU you are running on and if the JVM has optimized that code path yet. The first queries run in a client will be worse than later queries. Small queries can typically be translated in a millisecond or two while larger queries can take tens of milliseconds. In all cases tested the translation time is orders of magnitude smaller than the total runtime of the query.
See the entry on explain for details on how to measure this for your queries.
An Apache Spark plan is transformed and optimized into a set of operators called a physical plan. This plan is then run through a set of rules to translate it to a version that runs on the GPU. If you want to know what will run on the GPU and what will not along with an explanation why you can set spark.rapids.sql.explain to ALL. If you just want to see the operators not on the GPU you may set it to NOT_ON_GPU (which is the default setting value). Be aware that some queries end up being broken down into multiple jobs, and in those cases a separate log message might be output for each job. These are logged each time a query is compiled into an RDD, not just when the job runs. Because of this calling explain on a DataFrame will also trigger this to be logged.
The format of each line follows the pattern
indicator operation<NAME> operator? explanation
In this indicator is one of the following:
*for operations that will run on the GPU@for operations that could run on the GPU but will not because they are a part of a larger section of the plan that will not run on the GPU#for operations that have been removed from the plan. The reason they are removed will be in the explanation.!for operations that cannot run on the GPU
operation indicates the type of the operator.
ExpressionThese are typically functions that operate on columns of data and produce a column of data.ExecThese are higher level operations that operate on an entire table at a time.PartitioningThese are different types of partitioning used when reorganizing data to move to different tasks.InputThese are different input formats used with a few input statements, but not all.OutputThese are different output formats used with a few output statements, but not all.NOT_FOUNDThese are for anything that the plugin has no replacement rule for.
NAME is the name of the operator given by Spark.
operator? is an optional string representation of the operator given
by Spark.
explanation is a text explanation saying if this will
Generally if an operator is not compatible with Spark for some reason and is off, the explanation will include information about how it is incompatible and what configs to set to enable the operator if you can accept the incompatibility.
These messages are logged at the WARN level so even in spark-shell which by default only logs at WARN or above you should see these messages.
This translation takes place in two steps. The first step looks at the plan, figures out what can be translated to the GPU, and then does the translation. The second step optimizes the transitions between the CPU and the GPU. Explain will also log how long these translations took at the INFO level with lines like.
INFO GpuOverrides: Plan conversion to the GPU took 3.13 ms
INFO GpuOverrides: GPU plan transition optimization took 1.66 ms
Because it is at the INFO level, the default logging level for spark-shell is not going to display this information. If you want to monitor this number for your queries you might need to adjust your logging configuration.
Typically, there is a one to one mapping between CPU stages in a plan and GPU stages. There are a few places where this is not the case.
WholeStageCodeGen- The GPU plan typically does not do code generation, and does not support generating code for an entire stage in the plan. Code generation reduces the cost of processing data one row at a time. The GPU plan processes the data in a columnar format, so the costs of processing a batch is amortized over the entire batch of data and code generation is not needed.ColumnarToRowandRowToColumnartransitions - The CPU version of Spark plans typically process data in a row based format. The main exception to this is reading some kinds of columnar data, like Parquet. Transitioning between the CPU and the GPU also requires transitioning between row and columnar formatted data.GpuCoalesceBatchesandGpuShuffleCoalesce- Processing data on the GPU scales sublinearly. That means doubling the data does often takes less than half the time. Because of this we want to process larger batches of data when possible. These operators will try to combine smaller batches of data into fewer, larger batches to process more efficiently.SortMergeJoin- The RAPIDS Accelerator does not support sort merge joins yet. For now, we translate sort merge joins into shuffled hash joins. Because of this there are times when sorts may be removed or other sorts added to meet the ordering requirements of the query.TakeOrderedAndProject- TheTakeOrderedAndProjectoperator will take the top N entries in each task, shuffle the results to a single executor and then take the top N results from that. The GPU plan often has more metrics than the CPU versions do, and when we tried to combine all of these operations into a single stage the metrics were confusing to understand. Instead, we split the single stage up into multiple smaller parts, so the metrics are clearer.
Apache Spark caches what is used to build the output of the explain() function. That cache has no knowledge about configs, so it may return results that are not up to date with the current config settings. This is true of all configs in Spark. If you changed spark.sql.autoBroadcastJoinThreshold after running explain() on a DataFrame, the resulting query would not change to reflect that config and still show a SortMergeJoin even though the new config might have changed to be a BroadcastHashJoin instead. When actually running something like with collect, show or write a new DataFrame is constructed causing Spark to re-plan the query. This is why spark.rapids.sql.enabled is still respected when running, even if explain shows stale results.
The RAPIDS Accelerator does not change the way failures are normally handled by Apache Spark.
Technically the Spark scheduler does not make those decisions. The plugin has a set of rules that decide if an operation can safely be replaced by a GPU enabled version. We are working on some cost based optimizations to try and improve performance for some situations where it might be more efficient to stay on the CPU instead of going back and forth.
Yes, DPP works.
Any operation that is supported on GPU will stay on the GPU when AQE is enabled.
When running an explain() on a query where AQE is on, it is possible that AQE has not finalized the plan. In this case a message stating AdaptiveSparkPlan isFinalPlan=false will be printed at the top of the physical plan, and the explain output will show the query plan with CPU operators. As the query runs, the plan on the UI will update and show operations running on the GPU. This can happen for any AdaptiveSparkPlan where isFinalPlan=false.
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- ...
Once the query has been executed you can access the finalized plan on WebUI and in the user code running on the Driver, e.g. in a REPL or notebook, to confirm that the query has executed on GPU:
>>> df=spark.range(0,100).selectExpr("sum(*) as sum")
>>> df.explain()
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[], functions=[sum(id#0L)])
+- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#11]
+- HashAggregate(keys=[], functions=[partial_sum(id#0L)])
+- Range (0, 100, step=1, splits=16)
>>> df.collect()
[Row(sum=4950)]
>>> df.explain()
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=true
+- == Final Plan ==
GpuColumnarToRow false
+- GpuHashAggregate(keys=[], functions=[gpubasicsum(id#0L, LongType, false)]), filters=ArrayBuffer(None))
+- GpuShuffleCoalesce 2147483647
+- ShuffleQueryStage 0
+- GpuColumnarExchange gpusinglepartitioning$(), ENSURE_REQUIREMENTS, [id=#64]
+- GpuHashAggregate(keys=[], functions=[partial_gpubasicsum(id#0L, LongType, false)]), filters=ArrayBuffer(None))
+- GpuRange (0, 100, step=1, splits=16)
+- == Initial Plan ==
HashAggregate(keys=[], functions=[sum(id#0L)])
+- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#11]
+- HashAggregate(keys=[], functions=[partial_sum(id#0L)])
+- Range (0, 100, step=1, splits=16)
Yes. The RAPIDS Shuffle Manager in the MULTITHREADED mode (default) is compatible with ESS but the UCX mode is not.
Yes cache and persist are supported, the cache is GPU accelerated but still stored on the host memory. Please refer to RAPIDS Cache Serializer for more details.
No, that is not currently supported. It would require much larger changes to Apache Spark to be able to support this.
Yes, but we don’t actively test them, because the RAPIDS Accelerator hooks into Spark not at the various language APIs but at the Catalyst level after all the various APIs have converged into the DataFrame API.
Yes, but we don’t actively test them, because the RAPIDS Accelerator hooks into Spark not at the various language APIs but at the Catalyst level after all the various APIs have converged into the DataFrame API.
The GPU is not needed on the driver and there is no benefit to having one available on the driver for the RAPIDS Accelerator.
Yes, there is GPU support for Delta Lake and Apache Iceberg. See the additional support documentation for specifics on the operations supported for these formats.
There is no limit on the number of tasks per executor that you can run. Generally we recommend 2 to 6 tasks per executor and 1 GPU per executor. The GPU typically benefits from having 2 tasks run in parallel on it at a time, assuming your GPU has enough memory to support that. Having 2 to 3 times as many tasks off of the GPU as on the GPU allows for I/O to be run in parallel with the processing. If you increase the tasks too high you can overload the I/O and starting the initial processing can suffer. But if you have a lot of processing that cannot be done on the GPU, like complex UDFs, the more tasks you have the more CPU processing you can throw at it.
The spark.executor.cores and spark.task.resource.gpu.amount configuration settings are inputs to the Spark task scheduler and control the maximum number of tasks that can be run concurrently on an executor, regardless of whether they are running CPU or GPU code at any point in time. See the Number of Tasks per Executor section in the tuning guide for more details.
The spark.rapids.sql.concurrentGpuTasks configuration setting is specific to the RAPIDS Accelerator and further limits the number of concurrent tasks th
at are actively running code on the GPU or using GPU memory at any point in time. See the Number of Concurrent Tasks per GPU section of the tuning guide for more details.
The RAPIDS Accelerator only supports a single GPU per executor because that was a limitation of RAPIDS cudf, the foundation of the Accelerator. Basic support for working with multiple GPUs has only recently been added to RAPIDS cudf, and there are no plans for its individual operations to leverage multiple GPUs (e.g.: a single task’s join operation processed by multiple GPUs).
Many Spark setups avoid allocating too many concurrent tasks to the same executor, and often multiple executors are run per node on the cluster. Therefore this feature has not been prioritized, as there has not been a compelling use-case that requires it.
There are multiple reasons why this a problematic configuration:
Apache Spark does not support scheduling a fractional number of GPUs to an executor
CUDA context switches between processes sharing a single GPU can be expensive
Each executor would have a fraction of the GPU memory available for processing
Yes, but it requires support from the underlying cluster manager to isolate the MIG GPU instance for each executor (e.g.: by setting CUDA_VISIBLE_DEVICES, YARN with docker isolation or other means).
Note that MIG is not recommended for use with the RAPIDS Accelerator since it significantly reduces the amount of GPU memory that can be used by the Accelerator for each executor instance. If the cluster is purpose-built to run Spark with the RAPIDS Accelerator then we recommend running without MIG. Also note that the UCX-based shuffle plugin will not work as well in this configuration because MIG does not support direct GPU to GPU transfers.
However MIG can be advantageous if the cluster is intended to be shared amongst other processes (like ML / DL jobs).
The RAPIDS Accelerator provides the following solutions for running user-defined functions on the GPU:
RAPIDS Accelerated UDFs
UDFs can provide a RAPIDS accelerated implementation which allows the RAPIDS Accelerator to perform the operation on the GPU. See the RAPIDS accelerated UDF documentation for details.
Automatic Translation of Scala UDFs to Apache Spark Operations
The RAPIDS Accelerator has an experimental byte-code analyzer which can translate some simple Scala UDFs into equivalent Apache Spark operations in the query plan. The RAPIDS Accelerator then translates these operations into GPU operations just like other query plan operations.
The Scala UDF byte-code analyzer is disabled by default and must be enabled by the user via the spark.rapids.sql.udfCompiler.enabled configuration setting.
Optimize a row-based UDF in a GPU operation
If the UDF can not be implemented by RAPIDS Accelerated UDFs or be automatically translated to Apache Spark operations, the RAPIDS Accelerator has an experimental feature to transfer only the data it needs between GPU and CPU inside a query operation, instead of falling this operation back to CPU. This feature can be enabled by setting spark.rapids.sql.rowBasedUDF.enabled to true.
This can come down to a number of factors. The GPU version often compresses data in smaller chunks to get more parallelism and performance. This can result in larger files in some instances. We have also seen instances where the ordering of the data can have a big impact on the output size of the files. Spark tends to prefer sort based joins, and in some cases sort based aggregations, whereas the GPU versions are all hash based. This means that the resulting data can come out in a different order for the CPU and the GPU. This is not wrong, but can make the size of the output data different because of compression. Users can turn on spark.rapids.sql.hashOptimizeSort.enabled to have the GPU try to replicate more closely what the output ordering would have been if sort were used, like on the CPU.
When reading from a file that contains data referring to a particular timezone, e.g.: reading timestamps from an ORC file, the system’s timezone database at /usr/share/zoneinfo/ must contain the timezone in order to process the data properly. This error often indicates the system is missing the timezone database. The timezone database is provided by the tzdata package on many Linux distributions.
Caused by: ai.rapids.cudf.CudaException: OS call failed or operation not supported on this OS
at ai.rapids.cudf.Cuda.hostAllocPinned(Native Method)
at ai.rapids.cudf.PinnedMemoryPool.<init>(PinnedMemoryPool.java:254)
at ai.rapids.cudf.PinnedMemoryPool.lambda$initialize$1(PinnedMemoryPool.java:185)
at java.util.concurrent.FutureTask.run(FutureTask.java:264)
This is typically caused by the IOMMU being enabled. Please see the CUDA docs for this issue.
To fix it you can either disable the IOMMU, or you can disable using pinned memory by :ref`setting <general-configuration-table>` spark.rapids.memory.pinnedPool.size to 0.
Buffer overflow will happen when trying to serialize an object larger than spark.kryoserializer.buffer.max, and may result in an error such as:
Caused by: com.esotericsoftware.kryo.KryoException: Buffer overflow. Available: 0, required: 636
at com.esotericsoftware.kryo.io.Output.require(Output.java:167)
at com.esotericsoftware.kryo.io.Output.writeBytes(Output.java:251)
at com.esotericsoftware.kryo.io.Output.write(Output.java:219)
at java.base/java.io.ObjectOutputStream$BlockDataOutputStream.write(ObjectOutputStream.java:1859)
at java.base/java.io.ObjectOutputStream.write(ObjectOutputStream.java:712)
at java.base/java.io.BufferedOutputStream.write(BufferedOutputStream.java:123)
at java.base/java.io.DataOutputStream.write(DataOutputStream.java:107)
...
Try increasing the spark.kryoserializer.buffer.max from a default of 64M to something larger, for example 512M.
The RAPIDS Accelerator tracks GPU allocations that can be spilled to CPU memory or even disk using the RapidsBufferCatalog class.
When a fatal Spark exception happens, the executor process will not wait for task threads to complete before starting to shut down the process. This shutdown includes the RapidsBufferCatalog. This race can cause the catalog to log the following messages that can be ignored.
The beginning of a shutdown is logged as such:
INFO com.nvidia.spark.rapids.RapidsBufferCatalog: Closing storage
After shutdown starts, we could see acquire-after-free exception, where the storage already disposed of the object (GPU, host memory, and disk) and a task thread is not aware of the shutdown yet:
ERROR org.apache.spark.executor.Executor: Exception in task 1420.0 in stage 29.0 (TID 39681)
java.lang.IllegalStateException: Unable to acquire buffer for ID: TempSpillBufferId(3122,temp_local_a5806b35-3110-44f4-911c-39d55785e8f5)
Similarly, a double free is detected (where the first free is the catalog shutdown, and the second free is a task thread that freed as part of its logic), and will cause a WARN to be logged:
WARN com.nvidia.spark.rapids.RapidsDeviceMemoryStore: Trying to free an invalid buffer => TempSpillBufferId(3183,temp_local_e7f190f2-a5da-4dbf-b6ae-bd683089c102), size = 406702784, device memory buffer size=406702784
Yes, speculative execution in Spark is fine with the RAPIDS Accelerator plugin.
As with all speculative execution, it may or may not be beneficial depending on the nature of why a particular task is slow and how easily speculation is triggered. You should monitor your Spark jobs to see how often task speculation occurs and how often the speculating task (i.e.: the one launched later) finishes before the slow task that triggered speculation. If the speculating task often finishes first then that’s good, it is working as intended. If many tasks are speculating, but the original task always finishes first then this is a pure loss, the speculation is adding load to the Spark cluster with no benefit.
Below are some troubleshooting tips on GPU query performance issue: Identify the most time consuming part of the query. You can use the Profiling tool to process the Spark event log to get more insights of the query performance. For example, if I/O is the bottleneck, we suggest optimizing the backend storage I/O performance because the most suitable query type is computation bound instead of I/O or network bound.
Make sure at least the most time consuming part of the query is on the GPU. Please refer to Getting Started on Spark workload qualification for more details. Ideally we hope the whole query is fully on the GPU, but if some minor part of the query, eg. a small JDBC table scan, can not run on the GPU, it won’t cause much performance overhead. If there are some CPU fallbacks, check if those are some known features which can be enabled by turning on some RAPIDS Accelerator parameters. If the features needed do not exist in the most recent release of the RAPIDS Accelerator, please file a feature request with a minimum reproducing example.
Tune the Spark and RAPIDS Accelerator parameters such as
spark.sql.shuffle.partitions,spark.sql.files.maxPartitionBytesandspark.rapids.sql.concurrentGpuTasksas these configurations can affect performance of queries significantly. Please refer to Tuning Guide for more details.
If you are getting a warning Avro library not found by the RAPIDS Accelerator. or if you are getting the java.lang.NoClassDefFoundError: org/apache/spark/sql/v2/avro/AvroScan error, make sure you ran the Spark job by using the --jars or --packages option followed by the file path or maven path to RAPIDS jar since that is the preferred way to run RAPIDS accelerator.
Note, you can add locally installed jars for external packages such as Avro Data Sources and the RAPIDS Accelerator jars via spark.driver.extraClassPath (–driver-class-path in the client mode) on the driver side, and spark.executor.extraClassPath on the executor side. However, you should not mix the deploy methods for either of the external modules. Either deploy both Spark Avro and RAPIDS Accelerator jars as local jars via extraClassPath settings or use the --jars or --packages options.
As a consequence, per issue-5796, if you also use the RAPIDS Shuffle Manager, your deployment option may be limited to the extraClassPath method.
Starting from 22.06, the default value for spark.rapids.memory.gpu.pool is changed to ASYNC from ARENA for CUDA 11.5+. For CUDA 11.4 and older, it will fall back to ARENA.
In the 23.04 release of the accelerator two new exceptions were added to replace a regular OutOfMemoryError that was thrown before when the GPU ran out of memory. Originally we used OutOfMemoryError like on the CPU thinking that it would help to trigger GC in case handles pointing to GPU memory were leaked in the JVM heap. But OutOfMemoryError is technically a fatal exception and recovering from it is not strictly supported. As such Apache Spark treats it as a fatal exception and will kill the process that sees this exception. This can result in a lot of tasks being rerun if the GPU runs out of memory. These new exceptions prevent that. They also provide an indication to various GPU operators that the GPU ran out of memory and how that operator might be able to recover. RetryOOM indicates that the operator should roll back to a known good spot and then wait until the memory allocation framework decides that it should be retried. SplitAndRetryOOM is used when there is really only one task unblocked and the only way to recover would be to roll back to a goodspot and try to split the input so that less total GPU memory is needed.
These are not implemented for all GPU operations. A number of GPU operations that use a significant amount of memory have been updated to handle RetryOOM, but fewer have been updated to handle SplitAndRetryOOM. If you do run into these exceptions it is an indication that you are using too much GPU memory. The tuning guide can help you to reduce your memory usage. Be aware that some algorithms do not have a way to split their usage, things like window operations over some large windows. If tuning does not fix the problem please file an issue to help us understand what operators may need better out of core algorithm support.
The RAPIDS Accelerator for Apache Spark has several components that may or may not follow the encryption configurations that Apache Spark provides. The following documents the exceptions that are known at the time of writing this FAQ entry:
Local storage encryption (spark.io.encryption.enabled) is not supported for spilled buffers that the plugin uses to help with GPU out-of-memory situations. The RAPIDS Shuffle Manager does not implement local storage encryption for shuffle blocks when configured for UCX, but it does when configured in MULTITHREADED mode.
Network encryption (spark.network.crypto.enabled) is not supported in the RAPIDS Shuffle Manager when configured for UCX, but it is supported when configured in MULTITHREADED mode.
If your environment has specific encryption requirements for network or IO, please make sure that the RAPIDS Accelerator suits your needs, and file and issue or discussion if you have doubts or would like expanded encryption support.
RayDP provides simple APIs for running Spark on Ray. In order to run the RAPIDS Accelerator with RayDP, GPUs must be requested as GPU resources rather than gpu resources. This can be done with the following changes to the typical GPU resource scheduling setup with Spark:
Change the contents of the
getGpusResources.shscript to useGPUinstead ofgpuChange all of the configs that start with
spark.executor.resource.gpu.to corresponding configs that start withspark.executor.resource.GPU.Change
spark.task.resource.gpu.amounttospark.task.resource.GPU.amount
Note that the RAPIDS Accelerator is not regularly tested against Spark on Ray.
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