A few days ago I was asked about the performance improvements from using AVX-512 instructions in the Einstein Toolkit, i.e. the new machine instructions offered by Intel's Knights Landing and Skylake CPUs. I thought I should reply here in public since other might be interested as well.
If you want to learn more about what I write below, go to < http://agner.org/optimize/> and look at the file "microarchitecture.pdf". You might be aware that modern CPUs have instructions that execute multiple operations simultaneously, called SIMD (Single Instruction, Multiple Data). This is different from and in addition to the so-called "superscalar" execution, which allows executing multiple instructions (up to three or four) in a single cycle. Using SIMD instructions is an important ingredient to obtaining good performance in a compute-bound inner loop. The theoretical performance maximum of a single Skylake core can be calculated as follows: - use a fused multiply-add (FMA) instruction (i.e. use an instruction that calculates x*y+z as single operation), counting as 2 Flop - use SIMD to perform FMA operations on 8 vector elements simultaneously (factor 8 speedup) - arrange the code cleverly so that 2 of the 3 or 4 superscalar operations can be FMA (the others will need to be load or store instructions) (another factor 2 speedup) - run this e.g. at 2.5 GHz This leads to a theoretical performance maximum of 2 Flop * 8 * 2 * 2.5 GHz = 80 GFlop/s per core. If a compute node has 40 cores, then one can achieve 3.2 TFlop/s per node. Of course, these are all theoretical numbers, and all sorts of limitations will get in the way of achieving this. The point of my note here is to describe what some of these other limitations are, how to find out whether they are getting in the way, and if so, how to avoid them. If you are using a Knights Landing CPU (and if you are, you will know), then the most annoying limitation is that the CPU can in principle execute 4 instructions per cycle -- but it can, unfortunately, only decode 2 or so instructions per cycle. Now, the instructions that can be decoded can be more complicated than the instructions that are executed. For mainly historical reasons, the machine instructions that are read from memory by the CPU are first decoded and then split into micro-operations, and what is executed are actually micro-operations. If things go well, 2 instructions are read and split into 4 micro-operations. In practice, things don't usually go well, and the bottleneck of a calculation might actually be the part of the CPU that reads instructions from memory, not the part that executes them. However, this only applies to Knights Landing, not to Skylake: Skylake has no problems in this respect. Since it's quite difficult to analyze and remedy this, I will ignore this here. To make the compiler use FMA instructions instead of separate multiply and add instructions, it helps to use the "-ffast-math" flag. Without this flag, the compiler cannot apply the associativity laws for real numbers to floating-point numbers, and this might be necessary to re-order the operations in such a way that FMA instructions can be used. Since re-associating operations can change the rounding error that is incurred, the IEEE standard does not allow it, assuming (quite preposterously in most cases) that the programmer put great care in how floating-point expressions are written. For example, the expression $x + y + z$ is evaluated as $(x + y) + z$, and without "-ffast-math", this cannot be changed to $x + (y + z)$ or $(x + z) + y$. Of course, in the majority of all cases, the programmer didn't intend to enforce a particular order. Allowing the compiler to re-order expressions in such a way can improve performance for various reasons. Apart from being able to use FMA instructions, reordering can also improve instruction latency, or reduce the number of registers that are needed. One should also add that GCC and Clang do follow the IEEE standard by default, while the Intel compiler does not. So be careful when comparing performance between compilers. How can one check whether FMA instructions are used? The only reliable way is to look at the generated assembler output. To do so, compile the code (with optimizations enabled, of course), and locate the respective "*.o" file that contains e.g. the BSSN right-hand side, or the hydrodynamics flux calculations. The Unix command to disassemble an object file is "objdump" ("man objdump"). Try e.g. "objdump -d BSSN.o". It's best to redirect the output to another file, e.g. "BSSN.s", since it will be large (very large), and is best examined in an editor with a comfortable search function. On MacOS, the command to disassemble would be "otool -tv BSSN.o" The disassembled output might look somewhat like this: __ZL28PDstandardNthfdOrder223_implPKdDv4_dll: 0000000000000150 leaq (%rdi,%rsi), %rax 0000000000000154 vmovupd (%rax,%rdx), %ymm1 0000000000000159 movq %rsi, %rax 000000000000015c subq %rdx, %rax 000000000000015f vsubpd (%rdi,%rax), %ymm1, %ymm1 0000000000000164 movq %rdx, %rax 0000000000000167 subq %rsi, %rax 000000000000016a negq %rsi 000000000000016d subq %rdx, %rsi 0000000000000170 vsubpd (%rdi,%rax), %ymm1, %ymm1 0000000000000175 vaddpd (%rdi,%rsi), %ymm1, %ymm1 000000000000017a vmulpd %ymm0, %ymm1, %ymm0 000000000000017e retq This is the assembler code corresponding to a small function. The first line is an externally visible label, here a C++-"mangled" function name. You can use the command "c++filt" to convert this to a human-readable function signature. The other lines start with a number (the address in the object file), followed by a machine instruction and its arguments. There are literally hundreds of different machine instructions, usually with rather quaint semantics that only make sense in the context of 1976's technology <https://en.wikipedia.org/wiki/Intel_8086>. On Intel CPUs, the modern floating-point instructions start with the letter "v" for "vector". The second-to-last letter indicates whether this is a scalar ("s") or SIMD instruction ("p"). The last letter indicates whether this is a single precision ("s") or double precision ("d") operation. For example, "vsubpd" is SIMD floating point double-precision subtraction. There are three arguments -- the first two are the source operands, the last is the destination, i.e. where to the result is written. "%" indicates a register (which is fast to access), and registers have quaint names as well. The first letter of the floating-point register indicates whether a SIMD instruction access 128 bits ("x"), 256 bits ("y"), or 512 bits ("z"). Since a double precision number has 64 bits, the code here acts on 4-vectors. (This code is taken from my laptop, which does not support 512-bit vectors.) Parentheses indicate a pointer that is dereferenced, so e.g. "(%rdi,%rax)" means that the rdi and rax registers are first added, and the result is then interpreted as "double*" and dereferenced. Presumably, either rdi or rax contains a pointer, and the other contains an offset. Which is which needs to be inferred from context. <http://www.felixcloutier.com/x86/> lists all instructions and gives a detailed explanation. That's not a good resource for learning, but a good resource for the expert to study the details. Intel also provides manuals for their CPUs in the form of huge pdf files that are, however, much less fun to search. In this function, there are only four floating-point instructions altogether: vsubpd (twice), vaddpd, and vmulpd (obviously subtraction, addition, and multiplication). This function implements, in fact, a centred second-order finite difference operator evaluating a second derivative, namely a mixed derivative in the y-z-directions. There is also a "vmovupd" instruction in the beginning, which loads a value from memory into a register without performing a floating-point operation. The three additions and subtractions calculate the differences, the final multiplication takes the grid spacing into account. In this case, there is no fused multiply-add instruction, since it doesn't fit with the order of operations. Thus we're forced to forego at least half of the theoretical peak performance. Of course, the integer instructions in between the floating point instructions (mov, add ("lea" -- yes, instructions are quaint), sub, neg) also need to be executed, so that we can't keep the floating-point unit 100% busy anyway. Here is another function. (I am only showing really small functions here; actual functions can be hundreds or thousands of instructions long.) __ZL27PDstandardNthfdOrder61_implPKdDv4_dll.isra.7: 0000000000004e50 vmovapd (%rip), %ymm2 0000000000004e58 vmulpd 0x8(%rdi), %ymm2, %ymm2 0000000000004e5d vmovupd 0x10(%rdi), %ymm1 0000000000004e62 vmovupd -0x18(%rdi), %ymm3 0000000000004e67 vmovupd -0x10(%rdi), %ymm4 0000000000004e6c vmovupd 0x18(%rdi), %ymm5 0000000000004e71 vfmadd231pd (%rip), %ymm4, %ymm2 0000000000004e7a vfmsub132pd (%rip), %ymm3, %ymm1 0000000000004e83 vaddpd %ymm2, %ymm1, %ymm1 0000000000004e87 vmovupd -0x8(%rdi), %ymm2 0000000000004e8c vfmadd132pd (%rip), %ymm5, %ymm2 0000000000004e95 vaddpd %ymm2, %ymm1, %ymm1 0000000000004e99 vmulpd %ymm0, %ymm1, %ymm0 0000000000004e9d retq Note that the name of the function is interesting. It ends in ".isra.7", which indicates that this function was automatically generated by GCC by inter-procedural optimization < https://stackoverflow.com/questions/13963150/what-does-the-gcc-function-suffix-isra-mean>. GCC determined that it could copy the definition a function, and then modify the copy to adapt it to one particular place where it is called, producing a faster function. And this is indeed an optimized copy -- there are no integer operations at all! This is quite rare, which is why I'm showing it here. As above, the "vmovupd" instructions load data from memory, and the "0x8(%rdi)" syntax indicates a pointer dereference with an offset, here 8. In C, this would be "*(rdi+8)". This function calculates a 6th order accurate finite difference, namely the derivative in the x-direction. Here, there are FMA instructions present -- three of them, called "vfmadd231pd", "vfmsub132pd", and "vfmadd132pd". The "sub" variant calculates "x*y-z" (probably -- I didn't look it up). There are also variants (not used here) that calculate "-x*y+z" and "-x*y-z". The permutation of "123" in the instruction name indicates which of the 3 arguments corresponds to x, y, and z (I think -- I didn't look it up). Overall, while there are no integer operations, there are still a number of move instructions that load data from memory, so this function won't be achieving 100% of the theoretical floating point performance either. In my experience, there is always something getting in the way of achieving 100% of the theoretical floating-point performance. Achieving 20% on a Skylake CPU (or 10% on a Knights Landing CPU) is, in my opinion, very good, certainly at the level of using bold face in funding proposals or opening a bottle of Whisky from the last century. Of course, achieving 20% of the theoretical peak performance in a loop kernel is only an interesting achievement if a large fraction of the overall execution time is spent there. If most of the time is spent finding horizons or performing in I/O, or if there is a significant multi-threading overhead, then those problems need to be addressed instead. The issue that is most likely spoiling performance are memory accesses. Let's play another game with typical numbers: - memory bandwidth: 100 GByte/s - 8 byte/double - 2 memory banks ("sockets") per node - 20 cores per socket - memory needs to be accessed to both read inputs and write results - combined: 100 GByte/s / (8 byte/double) * 2 / 40 / 2 = 0.3 GMop/s That is, we can access about 0.3e9 double precision numbers per second per core. If we compare this to the 80e9 floating-point operations that a core can perform, and if we take these numbers as order of magnitude only (which is what they are), then we have a disparity of a factor of 100: For each double precision number read from or written to memory, we can -- on average -- perform 100 floating-point operations for free (i.e. in the same time). Put differently, each number read from memory needs to be re-used about 100 times. If not, then memory access is the bottleneck, and CPU floating point performance is simply irrelevant. The answer, of course, is using a cache. A cache is a very small but fast memory that sits in between the CPU and the memory. Caches are complicated (I won't go into details here), but in a first order approximation we can assume that a cache remembers that last n bytes that were read from or written to memory, and will provide instantaneous access to these data. To make things more complicated, Intel CPUs have several levels of cache daisy-chained. There is an L1 cache holding 32 kByte, and L2 cache holding 256 kByte or more, and an L3 cache holding several MByte. The L1 and L2 cache are specific to a particular core, the L3 cache is shared between all cores in the same NUMA domain, and has about 1 MByte per core. The L1 cache is so small that it can usually hold only data for a few grid points when evaluating the BSSN RHS (if at all), or a few cells for a hydro code. If you assume a SIMD vector length of 8, and 8 bytes per double, then the L1 cache can hold 512 separate values. Assume a bit of overhead etc., and you'll see that it's difficult to fit even a full BSSN RHS evaluation in there. If a single loop iteration doesn't fit into the L1 cache, then performance suffers dramatically, and it then makes sense to split the loop kernel into several, even if this involves repeating a few operations. For the BSSN equations, McLachlan uses 3 sequential loops instead of a single one, although the reason there is that this is required to make the instructions (!) fit into the L1 instruction cache (!), and isn't motivated by the size of the data cache. Instruction caches are even more complicated than data caches since it's not even officially documented what exactly is cached; however, heroic efforts have been made to reverse engineer this to be able to write efficient compilers. (See Agner's document above.) Assuming that a code's inner loop has been written in such a way that the temporary variables used during a single iteration fit into the L1 cache, the question is how data can be reused. Data reuse happens via derivative operators that access neighbouring points. The usual approach taken here is to use "loop tiling" (or "loop blocking"). In principle, compilers should be able to do this automatically; in practice, they do not. The idea is rather straightforward: One splits a large 3D loops into small tiles of a certain size (e.g. 8x4x4), and then has an outer loop nest iterating over tiles, and an inner loop nest iterating over the points within a tile. The inner loops can then be aggressively optimized, in particular if the number of iterations is known in advance. That might make it necessary to enforce that the total domain size is always a multiple of 8 (why not?), or one can add unused padding points at the domain edge. This can be well worth the performance benefit. A tile size of 8x4x4 allows using a single SIMD vector in the x direction, maybe (partially?) unrolling the loop in the y direction, etc. Obviously, the optimal tile sizes need to be determined via trial and error (cache behaviour is complicated and cannot really be predicted). The outer loop nest can then also be parallelized via OpenMP. Communication between OpenMP threads, which includes starting or stopping a thread etc., happens via the L3 caches that exchange data between each other. As a rule of thumb, this takes several hundred nanoseconds in the best possible case, and about 1 μs in practice (again, all numbers are orders of magnitude). Compare this to the floating-point performance, and it becomes clear that even handling a single tile might not be enough to offset multi-threading overhead. It makes sense to group multiple tiles into a group (e.g. a "pencil" of tiles in the x direction) that is dispatched by OpenMP. Regarding OpenMP: I find it is crucial for performance to bind OpenMP threads to a particular core during execution. The operating system usually does not do a good job, and whenever a thread moves, all its data need to be moved to higher cache levels or even the main memory, which is expensive. While memory is accessed in terms of bytes, this is these days merely a fantasy that is upheld for compatibility with programming languages such as C. Actual memory is highly parallel, and is fastest if accessed in multiples of a "cache line", which is today 64 bytes on high-end Intel CPUs. In other words, reading one byte reads 64 bytes from memory, and writing one byte requires first reading 64 bytes, then overwriting 1 byte, and then writing back 64 bytes. (Yes, writing 64 bytes can thus be faster than writing 1 byte.) If two cores write to the same cache line, then this cache line needs to be repeatedly read and written back to the L3 cache. This is also called "false sharing", and should be avoided in performance-critial multi-threaded code. (Reading from the same cache line is fine -- only writing is slow, since writing requires exclusive access.) To avoid this, it is necessary to align arrays to ensure that each tile starts at a multiple of 64 bytes. This can be done manually, and there are also special function posix_memalign for this. To ensure that not just the first point, but all x boundary points of a tile start at a multiple of 64 bytes, it is also necessary to ensure that the number of points in the x direction is a multiple of 8, padding the array with unused points if necessary. The abovementioned performance stumbling blocks have the endearing tendency to interact in highly nonlinear (read: almost random) ways. At times, changing one thing can have a dramatic unintended side effect onto another thing, which makes it difficult to understand what is going on. It can even be that making one thing worse can have a side effect that is several times larger, and which improves things overall. One has to be quite careful about various "effects" that people are finding. I recall a code with comments stating that copying the extrinsic curvature into a temporary array made things faster. By the time I examined the code, I could not confirm this. In principle, copying data uses more space in the cache and thus reduces performance. Who knows what other effects this copying had -- maybe the compiler treated the code differently, maybe it inhibited an "optimization" that made things worse in this case, etc. Blindly trying things to improve performance is good if it leads to an improved performance, but it cannot replace actual understanding of performance bottlenecks, and one should not fall prey to over-generalizing findings that worked in one case. If you are interested in understanding your code's performance, then the most important step I recommend it to measure how fast the code actually is, when compared to the theoretically possible performance. This is, unfortunately, quite tedious. One would hope that there are tools that simply output e.g. "5% of peak -- optimize these three functions", but HPC as a field is too far away from mainstream programming to offer such tools. Hence one has to calculate these numbers by hand. (And if a tool promises to measure these things automatically at run time, then be very careful: BY DEFINITION, this percentage cannot be measured at run time. See below.) The three traditionally important performance measures in HPC are: - floating-point operations (Flop/sec) - memory accesses (bytes/sec) - communication bandwidth (bytes/sec) Latencies are also important, but out-of-order execution of CPU instruction and memory accesses, as well as compiler optimizations, usually make these less important in practice. The exception is communication latency, which isn't optimized by the compiler since e.g. MPI communication is handled by a library and is not a built-in language feature. As a note -- be careful with units. I like to measure bandwidth in "double precision numbers per second", since that's what interests me. Industry standard is "bytes per second". Hardware vendors are easily tempted to quote "bits per second", since this leads to larger numbers. And since they want to sound clever, they never spell out quite what they mean. "GB/s" could mean either "GigaByte" or "GigaBit". (And then theres "GT/s", which are "GigaTransactions per second", where a "transaction" probably has 64 bits. Maybe. There's a long Wikipedia article about DDR memory.) For each of these three measures, the hardware imposes a hard limit. This limit can, with suitable study of literature, be determined ahead of time. There should also be benchmarks that are designed to measure the ideal case, and which should come close to obtaining this performance. For a typical HPC system, its web site will quote the first and third number, but not always the second. As life has it, the second (memory bandwidth) might actually be the most relevant one. It is also important to recall to which subsystem the performance measure applies: average memory bandwidth per core? per socket? per node? ... how is a socket defined? To see how code a particular code is doing, one needs to determine the requirements of the algorithm that the code implements. This is a property of the algorithm, not of the implementation. This is thus -- and I'm repeating myself here -- not something that can be measured at run time. In other words, one needs to count the number of USEFUL operations, not the number of operations that a code or a CPU performs after optimizations have been applied. (It would otherwise be very easy to artificially inflate the number of operations.) In fact, modern CPUs perform "speculative execution" of instructions, and in many cases, speculatively executed instructions yield invalid results that are then discarded. Obviously, speculatively executed instructions must not be counted when measuring the performance of a code. Compilers may also insert additional floating-point operations or memory accesses if they find it likely that this will speed up execution. Obviously, these additional operations must also not be counted. Thus, to determine the fraction of the theoretical peak performance that a code achieves, one needs to count (manually, most likely; or by using an instrumented C++ class) the number of floating-point operations and memory accesses that the algorithm (e.g. fourth-order finite differences with an fourth-order Runge-Kutta integrator) requires, and compare this to the running time of the code and the theoretical peak performance of the system. This tells you how far off you are, and also which of the performance criteria (floating-point, memory accesses, etc.) are the bottleneck. To make a last attempt to drive my point home: Measuring "fraction of peak performance" only makes sense if one counts only the useful operations, not if one counts the operations that happened to be executed at run time. A good tool for seeing which machine instructions take how long is "perf". perf <https://perf.wiki.kernel.org/> is a Linux utility that is distributed with the Linux kernel. You can use "perf record" when running an executable (for a few seconds -- more is probably not needed), and "perf report" to interactively analyze the results. - if much time is spent in "div" or "sqrt" or "exp" instructions, then using approximate SIMD implementations might help - if there are a few slow instructions that access memory, then optimizing cache use might help - if the slow instructions are spread all over the place, then the code might be well balanced, and further optimizations are difficult - perf can be used to measure any number of things, such as e.g. CPU cycles (run time), memory accesses, cache misses, etc. Good luck! -erik -- Erik Schnetter <eschnet...@perimeterinstitute.ca> http://www.perimeterinstitute.ca/personal/eschnetter/
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