PNaCl C/C++ Language Support

Source language support

The currently supported languages are C and C++. The PNaCl toolchain is based on recent Clang, which fully supports C++11 and most of C11. A detailed status of the language support is available here.

For information on using languages other than C/C++, see the FAQ section on other languages.

As for the standard libraries, the PNaCl toolchain is currently based on libc++, and the newlib standard C library. libstdc++ is also supported but its use is discouraged; see C++ standard libraries for more details.


Version information can be obtained:

  • Clang/LLVM: run pnacl-clang -v.
  • newlib: use the _NEWLIB_VERSION macro.
  • libc++: use the _LIBCPP_VERSION macro.
  • libstdc++: use the _GLIBCXX_VERSION macro.

Preprocessor definitions

When compiling C/C++ code, the PNaCl toolchain defines the __pnacl__ macro. In addition, __native_client__ is defined for compatibility with other NaCl toolchains.

Memory Model and Atomics

Memory Model for Concurrent Operations

The memory model offered by PNaCl relies on the same coding guidelines as the C11/C++11 one: concurrent accesses must always occur through atomic primitives (offered by atomic intrinsics), and these accesses must always occur with the same size for the same memory location. Visibility of stores is provided on a happens-before basis that relates memory locations to each other as the C11/C++11 standards do.

Non-atomic memory accesses may be reordered, separated, elided or fused according to C and C++’s memory model before the pexe is created as well as after its creation. Accessing atomic memory location through non-atomic primitives is Undefined Behavior.

As in C11/C++11 some atomic accesses may be implemented with locks on certain platforms. The ATOMIC_*_LOCK_FREE macros will always be 1, signifying that all types are sometimes lock-free. The is_lock_free methods and atomic_is_lock_free will return the current platform’s implementation at translation time. These macros, methods and functions are in the C11 header <stdatomic.h> and the C++11 header <atomic>.

The PNaCl toolchain supports concurrent memory accesses through legacy GCC-style __sync_* builtins, as well as through C11/C++11 atomic primitives and the underlying GCCMM __atomic_* primitives. volatile memory accesses can also be used, though these are discouraged. See Volatile Memory Accesses.

PNaCl supports concurrency and parallelism with some restrictions:

  • Threading is explicitly supported and has no restrictions over what prevalent implementations offer. See Threading.
  • volatile and atomic operations are address-free (operations on the same memory location via two different addresses work atomically), as intended by the C11/C++11 standards. This is critical in supporting synchronous “external modifications” such as mapping underlying memory at multiple locations.
  • Inter-process communication through shared memory is currently not supported. See Future Directions.
  • Signal handling isn’t supported, PNaCl therefore promotes all primitives to cross-thread (instead of single-thread). This may change at a later date. Note that using atomic operations which aren’t lock-free may lead to deadlocks when handling asynchronous signals. See Future Directions.
  • Direct interaction with device memory isn’t supported, and there is no intent to support it. The embedding sandbox’s runtime can offer APIs to indirectly access devices.

Setting up the above mechanisms requires assistance from the embedding sandbox’s runtime (e.g. NaCl’s Pepper APIs), but using them once setup can be done through regular C/C++ code.

Atomic Memory Ordering Constraints

Atomics follow the same ordering constraints as in regular C11/C++11, but all accesses are promoted to sequential consistency (the strongest memory ordering) at pexe creation time. We plan to support more of the C11/C++11 memory orderings in the future.

Some additional restrictions, following the C11/C++11 standards:

  • Atomic accesses must at least be naturally aligned.
  • Some accesses may not actually be atomic on certain platforms, requiring an implementation that uses global locks.
  • An atomic memory location must always be accessed with atomic primitives, and these primitives must always be of the same bit size for that location.
  • Not all memory orderings are valid for all atomic operations.

Volatile Memory Accesses

The C11/C++11 standards mandate that volatile accesses execute in program order (but are not fences, so other memory operations can reorder around them), are not necessarily atomic, and can’t be elided. They can be separated into smaller width accesses.

Before any optimizations occur, the PNaCl toolchain transforms volatile loads and stores into sequentially consistent volatile atomic loads and stores, and applies regular compiler optimizations along the above guidelines. This orders volatiles according to the atomic rules, and means that fences (including __sync_synchronize) act in a better-defined manner. Regular memory accesses still do not have ordering guarantees with volatile and atomic accesses, though the internal representation of __sync_synchronize attempts to prevent reordering of memory accesses to objects which may escape.

Relaxed ordering could be used instead, but for the first release it is more conservative to apply sequential consistency. Future releases may change what happens at compile-time, but already-released pexes will continue using sequential consistency.

The PNaCl toolchain also requires that volatile accesses be at least naturally aligned, and tries to guarantee this alignment.

The above guarantees ease the support of legacy (i.e. non-C11/C++11) code, and combined with builtin fences these programs can do meaningful cross-thread communication without changing code. They also better reflect the original code’s intent and guarantee better portability.


Threading is explicitly supported through C11/C++11’s threading libraries as well as POSIX threads.

Communication between threads should use atomic primitives as described in Memory Model and Atomics.

setjmp and longjmp

PNaCl and NaCl support setjmp and longjmp without any restrictions beyond C’s.

C++ Exception Handling

PNaCl currently supports C++ exception handling through setjmp() and longjmp(), which can be enabled with the --pnacl-exceptions=sjlj linker flag (set with LDFLAGS when using Make). Exceptions are disabled by default so that faster and smaller code is generated, and throw statements are replaced with calls to abort(). The usual -fno-exceptions flag is also supported, though the default is -fexceptions. PNaCl will support full zero-cost exception handling in the future.

NaCl supports full zero-cost C++ exception handling.

Inline Assembly

Inline assembly isn’t supported by PNaCl because it isn’t portable. The one current exception is the common compiler barrier idiom asm("":::"memory"), which gets transformed to a sequentially consistent memory barrier (equivalent to __sync_synchronize()). In PNaCl this barrier is only guaranteed to order volatile and atomic memory accesses, though in practice the implementation attempts to also prevent reordering of memory accesses to objects which may escape.

PNaCl supports Portable SIMD Vectors, which are traditionally expressed through target-specific intrinsics or inline assembly.

NaCl supports a fairly wide subset of inline assembly through GCC’s inline assembly syntax, with the restriction that the sandboxing model for the target architecture has to be respected.

Portable SIMD Vectors

SIMD vectors aren’t part of the C/C++ standards and are traditionally very hardware-specific. Portable Native Client offers a portable version of SIMD vector datatypes and operations which map well to modern architectures and offer performance which matches or approaches hardware-specific uses.

SIMD vector support was added to Portable Native Client for version 37 of Chrome and more features, including performance enhancements, have been added in subsequent releases, see the Release Notes for more details.

Hand-Coding Vector Extensions

The initial vector support in Portable Native Client adds LLVM vectors and GCC vectors since these are well supported by different hardware platforms and don’t require any new compiler intrinsics.

Vector types can be used through the vector_size attribute:

#define VECTOR_BYTES 16
typedef int v4s __attribute__((vector_size(VECTOR_BYTES)));
v4s a = {1,2,3,4};
v4s b = {5,6,7,8};
v4s c, d, e;
c = a + b;  /* c = {6,8,10,12} */
d = b >> a; /* d = {2,1,0,0} */

Vector comparisons are represented as a bitmask as wide as the compared elements of all 0 or all 1:

typedef int v4s __attribute__((vector_size(16)));
v4s snip(v4s in) {
  v4s limit = {32,64,128,256};
  v4s mask = in > limit;
  v4s ret = in & mask;
  return ret;

Vector datatypes are currently expected to be 128-bit wide with one of the following element types, and they’re expected to be aligned to the underlying element’s bit width (loads and store will otherwise be broken up into scalar accesses to prevent faults):

Type Num Elements Vector Bit Width Expected Bit Alignment
uint8_t 16 128 8
int8_t 16 128 8
uint16_t 8 128 16
int16_t 8 128 16
uint32_t 4 128 32
int32_t 4 128 32
float 4 128 32

64-bit integers and double-precision floating point will be supported in a future release, as will 256-bit and 512-bit vectors.

Vector element bit width alignment can be stated explicitly (this is assumed by PNaCl, but not necessarily by other compilers), and smaller alignments can also be specified:

typedef int v4s_element   __attribute__((vector_size(16), aligned(4)));
typedef int v4s_unaligned __attribute__((vector_size(16), aligned(1)));

The following operators are supported on vectors:

unary +, -
++, --
+, -, *, /, %
&, |, ^, ~
>>, <<
!, &&, ||
==, !=, >, <, >=, <=

C-style casts can be used to convert one vector type to another without modifying the underlying bits. __builtin_convertvector can be used to convert from one type to another provided both types have the same number of elements, truncating when converting from floating-point to integer.

typedef unsigned v4u __attribute__((vector_size(16)));
typedef float v4f __attribute__((vector_size(16)));
v4u a = {0x3f19999a,0x40000000,0x40490fdb,0x66ff0c30};
v4f b = (v4f) a; /* b = {0.6,2,3.14159,6.02214e+23}  */
v4u c = __builtin_convertvector(b, v4u); /* c = {0,2,3,0} */

It is also possible to use array-style indexing into vectors to extract individual elements using [].

typedef unsigned v4u __attribute__((vector_size(16)));
template<typename T>
void print(const T v) {
  for (size_t i = 0; i != sizeof(v) / sizeof(v[0]); ++i)
    std::cout << v[i] << ' ';
  std::cout << std::endl;

Vector shuffles (often called permutation or swizzle) operations are supported through __builtin_shufflevector. The builtin has two vector arguments of the same element type, followed by a list of constant integers that specify the element indices of the first two vectors that should be extracted and returned in a new vector. These element indices are numbered sequentially starting with the first vector, continuing into the second vector. Thus, if vec1 is a 4-element vector, index 5 would refer to the second element of vec2. An index of -1 can be used to indicate that the corresponding element in the returned vector is a don’t care and can be optimized by the backend.

The result of __builtin_shufflevector is a vector with the same element type as vec1 / vec2 but that has an element count equal to the number of indices specified.

// identity operation - return 4-element vector v1.
__builtin_shufflevector(v1, v1, 0, 1, 2, 3)

// "Splat" element 0 of v1 into a 4-element result.
__builtin_shufflevector(v1, v1, 0, 0, 0, 0)

// Reverse 4-element vector v1.
__builtin_shufflevector(v1, v1, 3, 2, 1, 0)

// Concatenate every other element of 4-element vectors v1 and v2.
__builtin_shufflevector(v1, v2, 0, 2, 4, 6)

// Concatenate every other element of 8-element vectors v1 and v2.
__builtin_shufflevector(v1, v2, 0, 2, 4, 6, 8, 10, 12, 14)

// Shuffle v1 with some elements being undefined
__builtin_shufflevector(v1, v1, 3, -1, 1, -1)

One common use of __builtin_shufflevector is to perform vector-scalar operations:

typedef int v4s __attribute__((vector_size(16)));
v4s shift_right_by(v4s shift_me, int shift_amount) {
  v4s tmp = {shift_amount};
  return shift_me >> __builtin_shuffle_vector(tmp, tmp, 0, 0, 0, 0);


Auto-vectorization is currently not enabled for Portable Native Client, but will be in a future release.

Undefined Behavior

The C and C++ languages expose some undefined behavior which is discussed in PNaCl Undefined Behavior.


PNaCl exposes 32-bit and 64-bit floating point operations which are mostly IEEE-754 compliant. There are a few caveats:

  • Some floating-point behavior is currently left as undefined.
  • The default rounding mode is round-to-nearest and other rounding modes are currently not usable, which isn’t IEEE-754 compliant. PNaCl could support switching modes (the 4 modes exposed by C99 FLT_ROUNDS macros).
  • Signaling NaN never fault.
  • Fast-math optimizations are currently supported before pexe creation time. A pexe loses all fast-math information when it is created. Fast-math translation could be enabled at a later date, potentially at a perf-function granularity. This wouldn’t affect already-existing pexe; it would be an opt-in feature.

    • Fused-multiply-add have higher precision and often execute faster; PNaCl currently disallows them in the pexe because they aren’t supported on all platforms and can’t realistically be emulated. PNaCl could (but currently doesn’t) only generate them in the backend if fast-math were specified and the hardware supports the operation.
    • Transcendentals aren’t exposed by PNaCl’s ABI; they are part of the math library that is included in the pexe. PNaCl could, but currently doesn’t, use hardware support if fast-math were provided in the pexe.

Computed goto

PNaCl supports computed goto, a non-standard GCC extension to C used by some interpreters, by lowering them to switch statements. The resulting use of switch might not be as fast as the original indirect branches. If you are compiling a program that has a compile-time option for using computed goto, it’s possible that the program will run faster with the option turned off (e.g., if the program does extra work to take advantage of computed goto).

NaCl supports computed goto without any transformation.

Future Directions

Inter-Process Communication

Inter-process communication through shared memory is currently not supported by PNaCl/NaCl. When implemented, it may be limited to operations which are lock-free on the current platform (is_lock_free methods). It will rely on the address-free properly discussed in Memory Model for Concurrent Operations.

POSIX-style Signal Handling

POSIX-style signal handling really consists of two different features:

  • Hardware exception handling (synchronous signals): The ability to catch hardware exceptions (such as memory access faults and division by zero) using a signal handler.

    PNaCl currently doesn’t support hardware exception handling.

    NaCl supports hardware exception handling via the <nacl/nacl_exception.h> interface.

  • Asynchronous interruption of threads (asynchronous signals): The ability to asynchronously interrupt the execution of a thread, forcing the thread to run a signal handler.

    A similar feature is thread suspension: The ability to asynchronously suspend and resume a thread and inspect or modify its execution state (such as register state).

    Neither PNaCl nor NaCl currently support asynchronous interruption or suspension of threads.

If PNaCl were to support either of these, the interaction of volatile and atomics with same-thread signal handling would need to be carefully detailed.