CUBAR

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[[File:TheG8.png|right|thumb|CUDA'd-up beyond all repair]]

[[CUDA]] (and [http://en.wikipedia.org/wiki/GPGPU General-Purpose Graphics Processing Unit] programming in general) is rapidly becoming a mainstay of high-performance computing. As CUDA and OpenCL move off of the workstation, and into the server -- off of the console, and into the cluster -- the security of these systems will become critical parts of the associated [http://en.wikipedia.org/wiki/Trusted_computing_base trusted computing base]. Even ignoring the issue of multiuser security, the properties of isolation and (to a lesser extent) confidentiality are important for debugging, profiling and reproducibility. I've authored the ~~<s>cudafucker</s>~~ CUBAR set of tools to investigate the security properties -- primarily the means and parameters of memory protection, and the division of protection between soft- and hardware -- of [[CUDA]] on NVIDIA hardware since the G80 architecture.

[[CUDA]] (and [http://en.wikipedia.org/wiki/GPGPU General-Purpose Graphics Processing Unit] programming in general) is rapidly becoming a mainstay of high-performance computing. As CUDA and OpenCL move off of the workstation, and into the server -- off of the console, and into the cluster -- the security of these systems will become critical parts of the associated [http://en.wikipedia.org/wiki/Trusted_computing_base trusted computing base]. Even ignoring the issue of multiuser security, the properties of isolation and (to a lesser extent) confidentiality are important for debugging, profiling and reproducibility. I've authored the CUBAR set of tools to investigate the security properties -- primarily the means and parameters of memory protection, and the division of protection between soft- and hardware -- of [[CUDA]] on NVIDIA hardware since the G80 architecture. This was essential information in the attempt to implement an open source CUDA software stack (see [[libcudest]]).

See our 2010 paper, "[[Media:Cubar2010.pdf|My Other Computer is Your GPU]]."

==Questions==

===Memory details===

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==Code==

* GitHub project: [http://github.com/dankamongmen/~~wdp/tree/master/cs8803ss-project/~~ dankamongmen:~~wdp/cs8803ss-project~~]

* GitHub project: [http://github.com/dankamongmen/cubar dankamongmen:cubar]

===Building===

Clone the [[git]] repository. Define (either via shell export or within a new file Makefile.local):

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==Tools==

[[File:Cudash-hyperboxl.png|right|thumb|cudash in use]]

===CUBAR tools===

Only those tools which launch CUDA kernels use the Runtime API. They might be converted to the Driver API.

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* <tt>cudaranger</tt> - Attempts to perform selectable memory accesses across the specified virtual address range of a device. Calculates the number of non-zero words.

It is necessary that <tt>cudadump</tt> spawn <tt>cudaranger</tt> rather than launching the kernels itself; a kernel failure results in all libcuda calls returning an early error (CUDA_ERROR_DEINITIALIZED (4) or CUDA_ERROR_LAUNCH_FAILURE (700)) for the remainder of a process's life (ie, creating a new context usually also fails '''FIXME: provide proof''').

====cudash====

The CUDA Shell (currently making use of the runtime api for kernel launches) facilitates stateful, invasive examination of the CUDA memory/process system. It has online help via the "help" command.

* ''FIXME'': document commands

===Other tools===

* [http://nouveau.freedesktop.org/wiki/REnouveau REnouveau], the Reverse-Engineering-for-nouveau tool

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* The [http://nouveau.freedesktop.org/wiki/IrcChatLogs Nouveau Development Companion]

* This Japanese [http://pc.watch.impress.co.jp/docs/2008/0617/kaigai446.htm article] has some great diagrams

* [[libcudest]], my effort to reverse engineer and reimplement libcuda.so

[[CATEGORY: GPGPU]]

[[CATEGORY: Projects]]

CUBAR: Difference between revisions

Dank (talk | contribs)

@@ Line 1: / Line 1: @@
 [[File:TheG8.png|right|thumb|CUDA'd-up beyond all repair]]
-[[CUDA]] (and [http://en.wikipedia.org/wiki/GPGPU General-Purpose Graphics Processing Unit] programming in general) is rapidly becoming a mainstay of high-performance computing. As CUDA and OpenCL move off of the workstation, and into the server -- off of the console, and into the cluster -- the security of these systems will become critical parts of the associated [http://en.wikipedia.org/wiki/Trusted_computing_base trusted computing base]. Even ignoring the issue of multiuser security, the properties of isolation and (to a lesser extent) confidentiality are important for debugging, profiling and reproducibility. I've authored the <s>cudafucker</s> CUBAR set of tools to investigate the security properties -- primarily the means and parameters of memory protection, and the division of protection between soft- and hardware -- of [[CUDA]] on NVIDIA hardware since the G80 architecture.
+[[CUDA]] (and [http://en.wikipedia.org/wiki/GPGPU General-Purpose Graphics Processing Unit] programming in general) is rapidly becoming a mainstay of high-performance computing. As CUDA and OpenCL move off of the workstation, and into the server -- off of the console, and into the cluster -- the security of these systems will become critical parts of the associated [http://en.wikipedia.org/wiki/Trusted_computing_base trusted computing base]. Even ignoring the issue of multiuser security, the properties of isolation and (to a lesser extent) confidentiality are important for debugging, profiling and reproducibility. I've authored the CUBAR set of tools to investigate the security properties -- primarily the means and parameters of memory protection, and the division of protection between soft- and hardware -- of [[CUDA]] on NVIDIA hardware since the G80 architecture. This was essential information in the attempt to implement an open source CUDA software stack (see [[libcudest]]).
+See our 2010 paper, "[[Media:Cubar2010.pdf|My Other Computer is Your GPU]]."
 ==Questions==
 ===Memory details===
@@ Line 74: / Line 75: @@
 ==Code==
-* GitHub project: [http://github.com/dankamongmen/wdp/tree/master/cs8803ss-project/ dankamongmen:wdp/cs8803ss-project]
+* GitHub project: [http://github.com/dankamongmen/cubar dankamongmen:cubar]
 ===Building===
 Clone the [[git]] repository. Define (either via shell export or within a new file Makefile.local):
@@ Line 81: / Line 82: @@
 ==Tools==
+[[File:Cudash-hyperboxl.png|right|thumb|cudash in use]]
 ===CUBAR tools===
 Only those tools which launch CUDA kernels use the Runtime API. They might be converted to the Driver API.
@@ Line 94: / Line 96: @@
 * <tt>cudaranger</tt> - Attempts to perform selectable memory accesses across the specified virtual address range of a device. Calculates the number of non-zero words.
 It is necessary that <tt>cudadump</tt> spawn <tt>cudaranger</tt> rather than launching the kernels itself; a kernel failure results in all libcuda calls returning an early error (CUDA_ERROR_DEINITIALIZED (4) or CUDA_ERROR_LAUNCH_FAILURE (700)) for the remainder of a process's life (ie, creating a new context usually also fails '''FIXME: provide proof''').
+====cudash====
+The CUDA Shell (currently making use of the runtime api for kernel launches) facilitates stateful, invasive examination of the CUDA memory/process system. It has online help via the "help" command.
+* ''FIXME'': document commands
 ===Other tools===
 * [http://nouveau.freedesktop.org/wiki/REnouveau REnouveau], the Reverse-Engineering-for-nouveau tool
@@ Line 112: / Line 117: @@
 * The [http://nouveau.freedesktop.org/wiki/IrcChatLogs Nouveau Development Companion]
 * This Japanese [http://pc.watch.impress.co.jp/docs/2008/0617/kaigai446.htm article] has some great diagrams
+* [[libcudest]], my effort to reverse engineer and reimplement libcuda.so
 [[CATEGORY: GPGPU]]
+[[CATEGORY: Projects]]