This paper reports a performance-based comparison of six state-of-the-art C/C++ compilers: AOCC, Clang, G++, Intel C++ compiler, PGC++, and Zapcc. We measure two aspects of the compilers’ performance:
- The speed of compiled C/C++ code parallelized with OpenMP 4.x directives for multi-threading and vectorization.
- The compilation time for large projects with heavy C++ templating.
In addition to measuring the performance, we interpret the results by examining the assembly instructions produced by each compiler.
The tests are performed on an Intel Xeon Platinum processor featuring the Skylake architecture with AVX-512 vector instructions.
Table of Contents
- 1. The Importance of a Good Compiler
- 2. Testing Methodology
- 2.1. Meet the Compilers
- 2.2. Target Architecture
- 2.3. Computational Kernels
- 2.4. Compilation Time
- 2.5. Test Details
- 2.6. Test Platform
- 2.7. Code Analysis
- 3. Results
- 3.1. Performance of Compiled Code
- 3.2. Compilation Speed
- 4. Summary
- Appendix A. LU Decomposition
- Appendix B. Jacobi Solver
- Appendix C. Structure Function
- Appendix D. Compilation Speed
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1. The Importance of a Good Compiler
Modern x86-64 CPUs are highly complex CISC architecture machines. Modern vector extensions to the x86-64 architecture, such as AVX2 and AVX-512, have instructions developed to handle common computational kernels. For example, the fused multiply-add instruction is used to increase the performance and accuracy in dense linear algebra, collision detection instruction is suitable for the operation of binning in statistical calculations, and bit-masked instructions are designed for handling branches in vector calculations. However, workloads with complex memory access patterns and non-standard kernels require considerable work from both the programmer and the compiler in order to achieve the highest performance.
At the same time, modern language standards work hard to abstract away the details of the underlying hardware and data structures and generate generic code that aligns more with logic and mathematics than instructions and memory locations. Newer language standards place greater emphasis on constructs that allow programmers the ability to express their intent. Modern standards of the C++ language are moving in the direction of greater expressivity and abstraction. The Python programming language is popular because of its readability and expressiveness, even at the cost of reduced runtime speed. Human-readable, expressive languages enable bug-free, maintainable code and are here to stay.
The consequence of increasing expressivity is an increased burden on the compiler to produce good assembly code from the complex high-level constructs written by the programmers. Compilers have to be smarter and work harder to wring the most performance out of code. Not all compilers are created equal and some are better than others at taking the same piece of code and producing efficient assembly.
In addition to producing fast executables, modern compilers must be fast themselves. Large software projects in C/C++ can span hundreds to thousands of individual translation units, each of which can be hundreds of lines in length. C++ code can also make heavy use of techniques such as template programming that require multiple passes from the compiler to produce object files. The compile time for large C++ projects can run into hours. Developers use practices like precompiled header files to reduce the compilation time. Large projects usually feature teams of developers with multiple interdependent changes being committed simultaneously. Each commit may require upstream developers to recompile significant portions of the codebase. Faster compilers are crucial to achieving high productivity from large teams.
Finally, there’s language extensions. Leveraging a modern computing system with multiple cores, vector processing capabilities, and accelerators goes beyond the natural capabilities of common programming languages. So frameworks specific to high-performance computing (HPC), such as OpenMP and OpenACC, step in to fill this gap. These frameworks offer APIs with which programmers can express parallelism in the code. The compiler, along with the corresponding runtime libraries, must map this parallel code onto the processor architecture. Numerous HPC projects rely on the OpenMP and OpenACC standards, and the standards are being expanded by the developers and hardware manufacturers. Therefore, compilers must constantly play catch up with the evolving standards for language extensions.
What is a good compiler? A good compiler should let us focus on the process of writing programs rather than struggling with the inadequacies of the compiler. It should compile the most recent language standards without complaint. We should feel confident in its ability to produce well-optimized code from even the most abstract codebase. Lastly, it should compile source code as quickly as possible.
2. Testing Methodology
2.1. Meet the Compilers
We aim to test the most commonly available C/C++ compilers. To be considered as a candidate for our test, the compiler must
- be listed on the Wikipedia list of C/C++ compilers,
- compile for the x86-64 architecture,
- be available for Linux platforms,
- be under active development.
The following six compilers pass our criteria:
- Intel C++ Compiler 18.0.0 (Intel C++ compiler)
- GNU Compiler Collection 7.2.0 (G++)
- PGI® C++ Compiler 17.4 (PGC++)
- Clang 5.0.0 (Clang)
- Zapcc Compiler 1.0.1 (Zapcc)
- AMD Optimizing C/C++ Compiler 1.0 (AOCC)
The Intel C++ compiler compiler is made by the Intel Corporation and is highly tuned for Intel processors. Intel C++ compiler features support for the latest C++ and OpenMP standards, as well as support for the latest Intel architectures. Intel C++ compiler is available free of charge for student and open source developers. For OpenMP support, by default it links against the Intel libiomp5.so library.
The G++ compiler is an open-source compiler made by the Free Software Foundation Inc. G++ was originally written to be the compiler for the GNU operating system. It is capable of generating code for a large number of target architectures and is widely available on Unix-like platforms. For OpenMP support, we link against the GNU libgomp.so library.
The following three compilers are based on the LLVM infrastructure for code generation.
The Clang compiler is developed by the LLVM project. The project aims to produce a fully G++Collection-compliant compiler suite that can replace the G++Collection. We use LLVM 5.0.0, the most recent release of the LLVM infrastructure. For better performance, we instruct Clang to use the Polly loop optimizer and the native lld linker. We link against the LLVM provided OpenMP library libomp.so.
Zapcc made by Ceemple Software Ltd. is a replacement for Clang that aims to compile code much faster than Clang. Zapcc uses the LLVM 5.0.0 backend for optimization, code generation, and also for libraries such as libomp.so.
AOCC is an AMD-tweaked version of the Clang 4.0.0 compiler optimized for the AMD Family 17h processors (‘Zen’ core). AMD enhancements include improved vectorization, high-level optimizer, whole program optimization, & code generation. The AMD compiler ships with its own versions of the LLVM libraries. For OpenMP support we link against the AMD provided libomp.so.
PGC++ is made by the Portland Group and features extensive support for the latest OpenACC 2.5 standard. PGC++ is available both as a free Community Edition (PGC++ 17.4) as well as a paid Professional Edition (PGC++ 17.9). At the time of writing, an LLVM-based beta edition with support for enables OpenMP 4.5 extensions is available for testing. The Portland Group is working to incorporate AVX-512 code generation abilities into PGC++. We show results obtained with the free Community Edition (PGC++ 17.4). PGC++ comes with its own set of libraries. For OpenMP support, we link against the PGI OpenMP library libpgmp.so
2.2. Target Architecture
The Intel Xeon Scalable processor family released in 2017 is based on the Skylake (SKL) microarchitecture, which succeeds the Broadwell (BDW) microarchitecture. The SKL microarchitecture introduces AVX-512 instructions, which feature twice the vector width and number of available vector registers as compared to AVX2 instructions available in the BDW microarchitecture. Properly written algorithms are capable of yielding 2x the performance on a SKL machine as compared to a BDW machine with a similar clock speed and core count.
All the tested compilers issue AVX-512 instructions except PGC++. AVX-512 support is under development for PGC++. Due to the lack of AVX-512 support, PGC++ performs substantially worse in some of the benchmarks than the other compilers.
2.3. Computational Kernels
To test the performance of compiled HPC code, we offer to the compilers three computational microkernels:
- A straightforward implementation of the pivotless LU decomposition with simple data structures and memory access pattern, and without any hand-tuning. Although this kernel can be optimized to the point at which it is compute bound, we test the un-optimized version of the kernel in order to determine how each compiler handles ‘naive’ source code with complex vectorization and threading patterns hidden within.
- A highly abstracted object-oriented implementation of a Jacobi solver for the Poisson problem on a square domain. This kernel tests how well the compilers can perform complex, cross-procedural code analysis to detect common parallel patterns (in this case, a 5-point stencil). This is a bandwidth-bound kernel, meaning that its performance is limited by the RAM bandwidth rather than by the floating-point arithmetic capabilities of the cores.
- A heavily-tuned implementation of structure function computation. We fully optimize this kernel to the point where it is compute-bound, i.e., limited by the arithmetic performance capabilities of the CPU. The purpose of this test is to see how efficient the resulting binary is when the source code is acutely aware of the underlying architecture.
We use OpenMP compiler extensions for vectorizing and parallelizing our computational kernels. OpenMP 3.1 extensions are supported by all 6 compilers. OpenMP 4.x introduces constructs for SIMD programming, including reduction and alignment clauses. OpenMP 4.x is supported by all the compilers with varying degrees of compliance with the exception of PGC++. Full OpenMP 4.5 support is forthcoming in a future LLVM-based version of PGC++. To level the playing field in anticipation of OpenMP 4.5 support by PGC++, we use the PGI-specific directive #pragma loop ivdep to inform PGC++ that loop iterations are independent for the following loop and can be safely executed simultaneously. This allows PGC++ to issue vector instructions for the following loop and has the same effect as the OpenMP 4.x directive #pragma omp simd for the other compilers.
2.4. Compilation Time
Our compile problem consists of compiling the TMV linear algebra library written by Dr. Mike Jarvis of the University of Pennsylvania. TMV stands for ‘templated matrix vector’. As the name suggests, this library contains templated linear algebra routines for use with various special matrix types. This makes TMV representative of scenarios where compilation speed is a significant factor in developer productivity.
2.5. Test Details
We provide the code used in this comparison in a GitHub repository at https://github.com/ColfaxResearch/CompilerComparisonCode. Each computational kernel is implemented in C++. The performance-critical function is located in the file ‘critical.cpp‘. The main function manages memory and calls the critical function to execute the computational kernel. We solve each problem NUM_PROBLEMS number of times to obtain the performance figure for a single ‘trial’. Each trial is repeated NUM_TRIALS number of times and the results of the first 3 trials discarded. The remaining trials are averaged to get the final performance figure for the run. Finally, we run each test NUM_RUNS number of times and select the most favorable result. This process ensures that we get consistent, reproducible results.
For multi-threaded performance, we empirically determine the optimal number of threads for each combination of the compiler, kernel, and problem size. I.e., we do not assume that the same thread count is optimal for code generated by all the compilers.
2.6. Test Platform
Our test platform is a 2-socket Intel Xeon Platinum 8168 machine (Skylake microarchitecture) with 48 physical cores at 2.7 GHz and 192 GB of RAM.
The theoretical peak performance of a system can be computed using
P = f × ncores × v × icyc × Finstruc, (1)
where P is the performance in GFLOP/s, f is the clock frequency in GHz, ncores is the number of available cores, v is the vector width, icyc is the number of instructions that can be executed per clock cycle, and Finstruc is the number of floating point operations performed per instruction.
As per the Intel Xeon Scalable family specifications, the maximum clock frequency of Platinum 8168 CPU is 2.5 GHz when executing AVX-512 instructions on 24 cores per socket. For double precision FMA, v=8 and Finstruc=2. Each Platinum 8168 CPU socket has 24 cores for a total of 48 cores on a 2-socket system. Each core has two FMA units, making icyc = 2. Therefore, the theoretical peak performance of our system for purely FMA double precision computations is
P×48FMA = 2.5 × 48 × 8 × 2 × 2 = 3840 GFLOP/s.
Non-FMA computational instructions such as vaddpd, vmulpd, and vsubpd also execute on the Skylake FMA units. However, each instruction only performs a single computation making Finstruc=1. Therefore, the theoretical peak performance of our system for purely non-FMA double precision computations is
P×48 = 2.5 × 48 × 8 × 2 × 1 = 1920 GFLOP/s.
When executing AVX-512 instructions with a single core, the maximum clock frequency of the Platinum 8168 CPU is 3.5 GHz. The corresponding theoretical peak performance is P×1FMA=112 GFLOP/s for purely FMA double precision computations and P×1=56 GFLOP/s for purely non-FMA double precision computations.
2.7. Code Analysis
We find that the best way to determine the reasons for performance differences between codes produced by the different compilers is by looking at the assembly instructions generated by each compiler. We generate assembly from the compiled object file for each compiler using objdump. Our command line for generating the assembly is
objdump -d -S -l -M intel critical.o &> critical.asm
This combination of flags outputs assembly using the Intel syntax. We edit the output assembly to remove extraneous information and compiler comments.
3.1. Performance of Compiled Code
In this section, we present and comment on the timing results. For an explanation of these values, please refer to the Appendices. There, we conduct a detailed analysis of the behavior of each computational kernel when compiled by the different compilers as well as a general overview of the kernels themselves. Compilation arguments and relevant snippets of code and assembly can also be found in the Appendices.
Table 1 summarizes our findings.
|Compiler||LU Serial||LU Parallel||Threads||Jacobi Serial||Jacobi Parallel||Threads||SF Serial||SF Parallel||Threads||TMV Compile|
Figure 1 plots the relative performance of the computational kernels when compiled by the different compilers and run with a single thread. The performance values are normalizes so that the performance of G++ is equal to 1.0. The normalization constant is different for different kernels.
Noticably, the LLVM 5.0.0 based Clang & Zapcc provide almost identical performance across the board. This is to be expected because the two compilers are very similar with the only difference being that Zapcc has been tweaked to improve the compile speed of Clang.
The PGI compiler provides higher performance than LLVM-based compilers in the first text, where the code has vectorization patterns, but is not optimized. While both G++ & Intel C++ compiler provide even higher performance, the behavior should be seen in light of the fact that PGC++ is incapable of issuing AVX-512 for the time being and is therefore using only half the vector width of the other compilers in this test.
The GNU and Intel compilers stand out in the second, bandwidth-bound test, where the data parallelism of a stencil operator is obscured by the abstraction techniques of the C++ language. These two are the only compilers that manage to successfully vectorize the computational kernel used in this test. The other compilers issue scalar instructions and therefore provide low performance.
The AMD & Intel compilers stand out in the third, compute-bound test, where the code is highly tuned for the SKL architecture. Both compilers manage to minimize reading and writing to memory. The other compilers use larger numbers of memory reads and writes, leading to lower performance. The PGI compiler, due to its current limitations, is issuing AVX2 instructions with half the vector width of the AVX-512 instructions issued by the other compilers.
Figure 2 shows the performance results with multi-threading. The plotted quantity is a relative performance measured with the optimal thread count for each compiler and kernel. Again, performance normalization is chosen so that the performance of G++ is equal to 1, and the normalization constant is specific to each kernel.
The relative performance of the compilers does not change much when running the structure function workloads with multiple threads. However, in the case of the Jacobi solver and LU decomposition kernels, the AMD compiler shows larger improvements relative to the other compilers. We believe that this is due to the AMD optimized OpenMP implementation used by AOCC.
3.2. Compilation Speed
Figure 3 shows the relative compilation time of the TMV library when compiled by the different compilers. The plotted values are the reciprocals of the compilation time, normalized so that G++ performance is equal to 1.
The Zapcc compiler is the fastest compiler in this test, handily beating the nearest competitor by a factor of more than 1.6x. The PGI compiler is the slowest compiler in the test. According to the Portland Group website, they are working on an LLVM-based update to the PGI compiler, which may with the compile time.
Different compilers produce executables with differing levels of performance even when given the same OpenMP standard-based source code to compile. The compile speed can also vary from compiler to compiler. We see a difference of
- 1.8x in performance between the best (Intel compiler) and worst compiler (Zapcc compiler) on our LU decomposition kernel (non-optimized, complex vectorization pattern).
- 3.5x in performance between the best (Intel compiler) and worst compiler (PGI compiler) on our Jacobi solver kernel (bandwidth-limited stencil obscured by abstraction techniques).
- 2.5x in performance between the best (Intel compiler) and worst compiler (LLVM clang compiler) on our Structure Function kernel (highly-tuned code for SKL).
- 5.4x in compile time between the best (Zapcc compiler) and worst compiler (PGI compiler) on our TMV compilation test (large templated library).
In the case of the computational kernels, the performance difference can be attributed to how each compiler implements the same sequence of arithmetic instructions. Different implementations require differing numbers of registers, memory operations, etc. that make some implementations faster and others slower. We discuss these details in the Appendices.
Modern CPUs are highly pipelined, superscalar machines that execute instructions out-of-order, use speculative execution, prefetching, and other performance-enhancing techniques. This makes it difficult to predict exactly how well a sequence of instructions will execute on any given microarchitecture. Hardware engineers use highly sophisticated simulations to overcome these problems when designing new CPUs. Compilers have to use heuristics to decide how to target specific CPU microarchitectures and thus have to be tuned to produce good code. Furthermore, the tuning is workload-specific, i.e., a generally sub-par compiler may produce the best code for certain workloads, even though it generally produces poorer code on average.
The tests in this work and our own experience suggest that the Intel compiler generally produces good code for a wide range of problems. Our computational kernels suggest that the Intel C++ compiler is generally able to provide the best performance because it has a better picture of the target machine architecture, i.e., it knows how to exploit all available registers, minimize memory operations, etc. Intel C++ compiler also has good support for the newer C++ and OpenMP standards. In our tests, Intel C++ compiler compiles complex code approximately 40% slower than G++. The Intel compiler has detailed documentation, code samples, and is able to output helpful optimization reports (see, e.g., this paper) that can be used to determine how to further improve application performance. Lastly, the Intel compiler is a part of a suite of libraries and tools, such as Intel MKL, Intel Advisor, Intel VTune Performance Analyzer, etc., which are very helpful for high-performance application development.
The GNU compiler also does very well in our tests. G++ produces the second fastest code in three out of six cases and is amongst the fastest compilers in terms of compile time. From a standards compliance standpoint, G++ has almost complete support for the new C++11 standard. GNU documentation is generally good, although it can be somewhat difficult to find details about obscure features. GNU optimization reports are exceedingly verbose making it very tedious to determine simple issues, such as if a given loop vectorized. Lastly, G++ has the added advantage of being open-source and freely available.
The LLVM-based Clang and Zapcc compilers produce executables with average performance but feature amongst the fastest compilers in the suite. The Zapcc is the fastest compiler in our compile test. LLVM and Clang have relatively good documentation, although it can be somewhat unclear as to which version of the product the documentation refers to. The Zapcc compiler relies entirely on the standard LLVM documentation. As LLVM matures, we expect the performance from all the LLVM-based compilers to keep increasing.
AMD’s AOCC compiler manages to tie with the Intel compiler in the compute-bound test and puts in a good showing in the Jacobi solver test. This is impressive given that AOCC is relatively new and targets an entirely different microarchitecture. At the moment, this compiler does not have much documentation, instead relying on LLVM documentation. We hope that AMD continues to improve AOCC and add high performance libraries for scientific computing as AOCC matures.
While all the LLVM-based compilers generate optimization reports, the reports do not contain enough helpful information about why the compiler chose to make certain decisions. This makes it unduly difficult to determine the best course of action to take to improve performance.
The PGI compiler’s strongest suit is its support for latest OpenACC 2.5 standard, which primarily applies to GPGPU programming. At the same time, PGC++ is capable of generating good x86 code as seen in the LU decomposition test. It lags behind its peers when it comes to support for new ISA extensions (AVX-512) and the latest OpenMP standards. Support for these features is in the works. The PGI compiler has good documentation and emits helpful and clear optimization reports. Like the Intel compiler, PGC++ comes with a suite of performance monitoring and optimization tools that add value for the developer.
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