Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.

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Masayuki Takagi




Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs. It provides not only FFI binding to CUDA driver API but the kernel description language with which users can define CUDA kernel functions in S-expression. The kernel description language also provides facilities to define kernel macros and kernel symbol macros in addition to kernel functions. Cl-cuda's kernel macro and kernel symbol macro offer powerful abstraction that CUDA C itself does not have and provide enormous advantage in resource-limited GPU programming.

Kernel functions defined with the kernel description language can be launched as almost same as ordinal Common Lisp functions except that they must be launched in a CUDA context and followed with grid and block sizes. Kernel functions are compiled and loaded automatically and lazily when they are to be launched for the first time. This process is as following. First, they are compiled into a CUDA C code (.cu file) by cl-cuda. The compiled CUDA C code, then, is compiled into a CUDA kernel module (.ptx file) by NVCC - NVIDIA CUDA Compiler Driver. The obtained kernel module is automatically loaded via CUDA driver API and finally the kernel functions are launched with properly constructed arguments to be passed to CUDA device. Since this process is autonomously managed by the kernel manager, users do not need to handle it for themselves. About the kernel manager, see Kernel manager section.

Memory management is also one of the most important things in GPU programming. Cl-cuda provides memory block data structure which abstract host memory and device memory. With memory block, users do not need to manage host memory and device memory individually for themselves. It lightens their burden on memory management, prevents bugs and keeps code simple. Besides memory block that provides high level abstraction on host and device memory, cl-cuda also offers low level interfaces to handle CFFI pointers and CUDA device pointers directly. With these primitive interfaces, users can choose to gain more flexible memory control than using memory block if needed.

Cl-cuda is verified on several environments. For detail, see Verification environments section.


Following code is a part of vector addition example using cl-cuda based on CUDA SDK's "vectorAdd" sample.

You can define vec-add-kernel kernel function using defkernel macro. In the definition, aref is to refer values stored in an array. set is to store values into an array. block-dim-x, block-idx-x and thread-idx-x have their counterparts in CUDA C's built-in variables and are used to specify the array index to be operated in each CUDA thread.

Once the kernel function is defined, you can launch it as if it is an ordinal Common Lisp function except that it requires to be in a CUDA context and followed by :gird-dim and :block-dim keyword parameters which specify the dimensions of grid and block. To keep a CUDA context, you can use with-cuda macro which has responsibility on initializing CUDA and managing a CUDA context. with-memory-blocks manages memory blocks which abstract host memory area and device memory area, then sync-memory-block copies data stored in a memory block between host and device.

For the whole code, please see examples/vector-add.lisp.

(defkernel vec-add-kernel (void ((a float*) (b float*) (c float*) (n int)))
  (let ((i (+ (* block-dim-x block-idx-x) thread-idx-x)))
    (if (< i n)
        (set (aref c i)
             (+ (aref a i) (aref b i))))))

(defun main ()
  (let* ((dev-id 0)
         (n 1024)
         (threads-per-block 256)
         (blocks-per-grid (/ n threads-per-block)))
    (with-cuda (dev-id)
      (with-memory-blocks ((a 'float n)
                           (b 'float n)
                           (c 'float n))
        (random-init a n)
        (random-init b n)
        (sync-memory-block a :host-to-device)
        (sync-memory-block b :host-to-device)
        (vec-add-kernel a b c n
                        :grid-dim  (list blocks-per-grid 1 1)
                        :block-dim (list threads-per-block 1 1))
        (sync-memory-block c :device-to-host)
        (verify-result a b c n)))))


You can install cl-cuda via quicklisp.

> (ql:quickload :cl-cuda)

You may encounter the following error, please install CFFI explicitly (ql:quickload :cffi) before loading cl-cuda. Just once is enough.

Component CFFI-GROVEL not found


Cl-cuda requires following:

  • NVIDIA CUDA-enabled GPU
  • CUDA Toolkit, CUDA Drivers and CUDA SDK need to be installed

Verification environments

Cl-cuda is verified to work in following environments:

Environment 1

  • Mac OS X 10.6.8 (MacBookPro)
  • GeForce 9400M
  • CUDA 4
  • SBCL 1.0.55 32-bit
  • All tests pass, all examples work


  • Amazon Linux x86_64 (Amazon EC2)
  • Tesla M2050
  • CUDA 4
  • SBCL 1.1.7 64-bit
  • All tests pass, all examples which are verified work (others not tried yet)
  • (setf *nvcc-options* (list "-arch=sm_20" "-m32")) needed

Environment3 (Thanks to Viktor Cerovski)

  • Linux 3.5.0-32-generic Ubuntu SMP x86_64
  • GeFroce 9800 GT
  • CUDA 5
  • SBCL 1.1.7 64-bit
  • All tests pass, all examples work

Environment4 (Thanks to wvxvw)

  • Fedra18 x86_64
  • GeForce GTX 560M
  • CUDA 5.5
  • SBCL 1.1.2-1.fc18
  • vector-add example works (didn't try the rest yet)

Further information:

  • (setf *nvcc-options* (list "-arch=sm_20" "-m32")) needed
  • using video drivers from rpmfusion instead of the ones in cuda package
  • see issue #1

Environment5 (Thanks to Atabey Kaygun)

  • Linux 3.11-2-686-pae SMP Debian 3.11.8-1 (2013-11-13) i686 GNU/Linux
  • NVIDIA Corporation GK106 GeForce GTX 660
  • CUDA 5.5
  • SBCL 1.1.12
  • All tests pass, all examples work

Environment6 (Thanks to @gos-k)

  • Ubuntu 16.04.1 LTS
  • GeForce GTX 1080
  • CUDA Version 8.0.27
  • Driver Version 367.35
  • CCL Version 1.11-r16635 (LinuxX8664)
  • All tests pass, all examples work


Here explain some APIs commonly used.

[Macro] with-cuda

WITH-CUDA (dev-id) &body body

Initializes CUDA and keeps a CUDA context during body. dev-id is passed to get-cuda-device function and the device handler returned is passed to create-cuda-context function to create a CUDA context in the expanded form. The results of get-cuda-device and create-cuda-context functions are bound to *cuda-device* and *cuda-context* special variables respectively. The kernel manager unloads before with-cuda exits.

[Function] synchronize-context


Blocks until a CUDA context has completed all preceding requested tasks.

[Function] alloc-memory-block


Allocates a memory block to hold size elements of type type and returns it. Actually, linear memory areas are allocated on both host and device memory and a memory block holds pointers to them.

[Function] free-memory-block

FREE-MEMORY-BLOCK memory-block

Frees memory-block previously allocated by alloc-memory-block. Freeing a memory block twice should cause an error.

[Macro] with-memory-block, with-memory-blocks

WITH-MEMORY-BLOCK (var type size) &body body
WITH-MEMORY-BLOCKS ({(var type size)}*) &body body

Binds var to a memory block allocated using alloc-memory-block applied to the given type and size during body. The memory block is freed using free-memory-block when with-memory-block exits. with-memory-blocks is a plural form of with-memory-block.

[Function] sync-memory-block

SYNC-MEMORY-BLOCK memory-block direction

Copies stored data between host memory and device memory for memory-block. direction is either :host-to-device or :device-to-host which specifies the direction of copying.

[Accessor] memory-block-aref

MEMORY-BLOCK-AREF memory-block index

Accesses memory-block's element specified by index. Note that the accessed memory area is that on host memory. Use sync-memory-block to synchronize stored data between host memory and device memory.

[Macro] defglobal

DEFGLOBAL name type &optional expression qualifiers

Defines a global variable. name is a symbol which is the name of the variable. type is the type of the variable. Optional expression is an expression which initializes the variable. Optional qualifiers is one of or a list of keywords: :device, :constant, :shared, :managed and :restrict, which are corresponding to CUDA C's __device__, __constant__, __shared__, __managed__ and __restrict__ variable qualifiers. If not given, :device is used.

(defglobal pi float 3.14159 :constant)

[Accessor] global-ref

Accesses a global variable's value on device from host with automatically copying its value from/to device.

(defglobal x :device int 0)
(global-ref x)                 ; => 0
(setf (global-ref x) 42)
(global-ref x)                 ; => 42

[Special Variable] *tmp-path*

Specifies the temporary directory in which cl-cuda generates files such as .cu file and .ptx file. The default is "/tmp/".

(setf *tmp-path* "/path/to/tmp/")

[Special Variable] *nvcc-options*

Specifies additional command line options passed to nvcc command that cl-cuda calls internally. The default is nil. If -arch=sm_XX option is not specified here, it is automatically inserted with cuDeviceComputeCapability driver API.

(setf *nvcc-options* (list "-arch=sm_20" "-m32"))

[Special Variable] *nvcc-binary*

Specifies the path to nvcc command so that cl-cuda can call internally. The default is just nvcc.

(setf *nvcc-binary* "/path/to/nvcc")

[Special Variable] *show-messages*

Specifies whether to let cl-cuda show operational messages or not. The default is t.

(setf *show-messages* nil)

[Special Variable] *sdk-not-found*

Readonly. The value is t if cl-cuda could not find CUDA SDK or at least it failed to load libcuda for some reason, otherwise nil.

*sdk-not-found*    ; => nil

Kernel Description Language


not documented yet.

IF statement

IF test-form then-form [else-form]

if allows the execution of a form to be dependent on a single test-form. First test-form is evaluated. If the result is true, then then-form is selected; otherwise else-form is selected. Whichever form is selected is then evaluated. If else-form is not provided, does nothing when else-form is selected.


(if (= a 0)
    (return 0)
    (return 1))


if (a == 0) {
  return 0;
} else {
  return 1;

LET statement

LET ({(var init-form)}*) statement*

let declares new variable bindings and set corresponding init-forms to them and execute a series of statements that use these bindings. let performs the bindings in parallel. For sequentially, use let* kernel macro instead.


(let ((i 0))
  (return i))


  int i = 0;
  return i;


SYMBOL-MACROLET ({(symbol expansion)}*) statement*

symbol-macrolet establishes symbol expansion rules in the variable environment and execute a series of statements that use these rules. In cl-cuda's compilation process, the symbol macros found in a form are replaces by corresponding expansions.


(symbol-macrolet ((x 1.0))
  (return x))


  return 1.0;

MACROLET statement

MACROLET ({(name lambda-list local-form*)}*) statement*

macrolet establishes local macro definitions, using the same format as defkernelmacro, and executes a series of statements with these definition bindings.


(macrolet ((square (a)
             (if (numberp a)
                 (* a a)
                 `(* ,a ,a))))
  (return (square 2)))


  return 4;

DO statement

DO ({(var init-form step-form)}*) (test-form) statement*

do iterates over a group of statements while test-form holds. do accepts an arbitrary number of iteration vars and their initial values are supplied by init-forms. step-forms supply how the vars should be updated on succeeding iterations through the loop.


(do ((a 0 (+ a 1))
     (b 0 (+ b 1)))
    ((> a 15))


for ( int a = 0, int b = 0; ! (a > 15); a = a + 1, b = b + 1 )


WITH-SHARED-MEMORY ({(var type size*)}*) statement*

with-shared-memory declares new variable bindings on shared memory by adding __shared__ variable specifiers. It allows to declare array variables if dimensions are provided. A series of statements are executed with these bindings.


(with-shared-memory ((a int 16)
                     (b float 16 16))


  __shared__ int a[16];
  __shared__ float b[16][16];

SET statement

SET reference expression

set provides simple variable assignment. It accepts one of variable, structure and array references as reference.


(set x 1.0)
(set (float4-x y 1.0)
(set (aref z 0) 1.0)


x = 1.0;
y.x = 1.0;
z[0] = 1.0;

PROGN statement

PROGN statement*

progn evaluates statements, in the order in which they are given.





RETURN statement

RETURN [return-form]

return returns control, with return-form if supplied, from a kernel function.


(return 0)


return 0;


The following figure illustrates cl-cuda's overall architecture.

                   | defkernel                       | memory    | context   |
       cl-cuda.api +---------------------------------+           |           |
                   | kernel-manager                  |           |           |
      cl-cuda.lang | Kernel description lang.   | the Compiler               |
cl-cuda.driver-api | driver-api                                              |
              CUDA | CUDA driver API                                         |

Cl-cuda consists of three subpackages: api, lang and driver-api.

driver-api subpackage is a FFI binding to CUDA driver API. api subpackage invokes CUDA driver API via this binding internally.

lang subpackage provides the kernel description language. It provides the language's syntax, type, built-in functions and the compiler to CUDA C. api subpackage calls this compiler.

api subpackage provides API for cl-cuda users. It further consists of context, memory, kernel-manager and defkernel subpackages. context subpackage has responsibility on initializing CUDA and managing CUDA contexts. memory subpackage offers memory management, providing high level API for memory block data structure and low level API for handling host memory and device memory directly. kernel-manager subpackage manages the entire process from compiling the kernel description language to loading/unloading obtained kernel module autonomously. Since it is wrapped by defkernel subpackage which provides the interface to define kernel functions, cl-cuda's users usually do not need to use it for themselves.

Kernel manager

The kernel manager is a module which manages defining kernel functions, compiling them into a CUDA kernel module, loading it and unloading it. I show you its work as a finite state machine here.

To begin with, the kernel manager has four states.

I   initial state
II  compiled state
III module-loaded state
IV  function-loaded state

The initial state is its entry point. The compiled state is a state where kernel functions defined with the kernel descrpition language have been compiled into a CUDA kernel module (.ptx file). The obtained kernel module has been loaded in the module-loaded state. In the function-loaded state, each kernel function in the kernel module has been loaded.

Following illustrates the kernel manager's state transfer.

      compile-module        load-module            load-function
    =================>    =================>     =================>
  I                    II                    III                    IV
    <=================    <=================
      define-function     <========================================
      define-macro          unload

kernel-manager-compile-module function compiles defined kernel functions into a CUDA kernel module. kernel-manager-load-module function loads the obtained kernel module. kernel-manager-load-function function loads each kernel function in the kernel module.

In the module-loaded state and function-loaded state, kernel-manager-unload function unloads the kernel module and turn the kernel manager's state back to the compiled state. kernel-manager-define-function, kernel-manager-define-macro, kernel-manager-define-symbol-macro and kernel-manager-define-global functions, which are wrapped as defkernel, defkernelmacro, defkernel-symbol-macro and defglobal macros respectively, change its state back into the initial state and make it require compilation again.

The kernel manager is stored in *kernel-manager* special variable when cl-cuda is loaded and keeps alive during the Common Lisp process. Usually, you do not need to manage it explicitly.

How cl-cuda works when CUDA SDK is not installed

This section is for cl-cuda users who develop an application or a library which has alternative sub system other than cl-cuda and may run on environments CUDA SDK is not installed.

Compile and load time Cl-cuda is compiled and loaded without causing any conditions on environments CUDA SDK is not installed. Since cl-cuda API 's symbols are interned, user programs can use them normally.

Run time At the time cl-cuda's API is called, an error that tells CUDA SDK is not found should occur. With *sdk-not-found* special variable, user programs can get if cl-cuda has found CUDA SDK or not.

How cl-cuda determines CUDA SDK is installed or not is that if it has successfully loaded libuda dynamic library with cffi:user-foreign-library function.


The low level interface works with multiple streams. With the async stuff it's possible to overlap copy and computation with two streams. Cl-cuda provides *cuda-stream* special variable, to which bound stream is used in kernel function calls.

The following is for working with streams in mgl-mat:

(defmacro with-cuda-stream ((stream) &body body)
  (alexandria:with-gensyms (stream-pointer)
         ((,stream-pointer 'cl-cuda.driver-api:cu-stream))
       (cl-cuda.driver-api:cu-stream-create ,stream-pointer 0)
       (let ((,stream (cffi:mem-ref ,stream-pointer
              (locally ,@body)
           (cl-cuda.driver-api:cu-stream-destroy ,stream))))))

then, call a kernel function with binding a stream to *cuda-stream*:

(with-cuda-stream (*cuda-stream*)



Copyright (c) 2012 Masayuki Takagi (


Licensed under the MIT License.

Dependencies (12)

  • alexandria
  • cffi
  • cl-emb
  • cl-opengl
  • cl-pattern
  • cl-ppcre
  • cl-reexport
  • external-program
  • imago
  • local-time
  • osicat
  • split-sequence

Dependents (1)

  • GitHub
  • Quicklisp