Related
I have some data that isn't stored as structure of arrays. What is the best practice for loading the data in registers?
__m128 _mm_set_ps (float e3, float e2, float e1, float e0)
// or
__m128 _mm_loadu_ps (float const* mem_addr)
With _mm_loadu_ps, I'd copy the data in a temporary stack array, vs. copying the data as values directly. Is there a difference?
It can be a tradeoff between latency and throughput, because separate stores into an array will cause a store-forwarding stall when you do a vector load. So it's high latency, but throughput could still be ok, and it doesn't compete with surrounding code for the vector shuffle execution unit. So it can be a throughput win if the surrounding code also has shuffle operations, vs. 3 shuffles to insert 3 elements into an XMM register after a scalar load of the first one. Either way it's still a lot of total uops, and that's another throughput bottleneck.
Most compilers like gcc and clang do a pretty good job with _mm_set_ps () when optimizing with -O3, whether the inputs are in memory or registers. I'd recommend it, except in some special cases.
The most common missed-optimization with _mm_set is when there's some locality between the inputs. e.g. don't do _mm_set_ps(a[i+2], a[i+3], a[i+0], a[i+1]]), because many compilers will use their regular pattern without taking advantage of the fact that 2 pairs of elements are contiguous in memory. In that case, use (the intrinsics for) movsd and movhps to load in two 64-bit chunks. (Not movlps: it merges into an existing register instead of zeroing the high elements, so it has a false dependency on the old contents while movsd zeros the high half.) Or a shufps if some reordering is needed between or within the 64-bit chunks.
The "regular pattern" that compilers use will usually be movss / insertps from memory if compiling with SSE4, or movss loads and unpcklps shuffles to combine pairs and then another unpcklps, unpcklpd, or movlhps to shuffle into one register. Or a shufps or shufpd if the compiler likes to waste code-side on immediate shuffle-control operands instead of using fixed shuffles intelligently.
See also Agner Fog's optimization guides for some handy tables of data-movement instructions to get a better idea of what the compiler has to work with, and how stuff performs. Note that Haswell and later can only do 1 shuffle per clock. Also other links in the x86 tag wiki.
There's no really cheap way for a compiler or human to do this, in the general case when you have 4 separate scalars that aren't contiguous in memory at all. Or for register inputs, where it can't optimize the way they're generated in registers in the first place to have some of them already packed together. (e.g. for function args passed in registers to a function that can't / doesn't inline.)
Anyway, it's not a big deal unless you have this inside an inner loop. In that case, definitely worry about it (and check the compiler's asm output to see if it made a mess or could do better if you program the gather yourself with intrinsics that map to single instructions like _mm_load_ss / _mm_shuffle_ps).
If possible, rearrange your data layout to make data contiguous in at least small chunks / stripes. (See https://stackoverflow.com/tags/sse/info, specifically these slides. But sometimes one part of the program needs the data one way, and the other needs another. Choose the layout that's good for the case that needs to be faster, or that runs more often, or whatever, and suck it up and do the best you can for the other part of the program. :P Possibly transpose / convert once to set up for multiple SIMD operations, but extra passes over data with no computation just suck up time and can hurt your computational intensity (how much ALU work you do for each time you load data into registers) more than they help.
And BTW, actual gather instructions (like AVX2 vgatherdps) are not very fast; even on Skylake it's probably not worth using a gather instruction for four 32-bit elements at known locations. On Broadwell / Haswell, gather is definitely not worth using for this.
I have made a design in Quartus-II, on which I take an arrange of 57.600, 32 bits binary numbers, process it (some simple arithmetic) and then output another arrange of 57.600, 32 bits binary numbers (3 sets). For the input of data I used readmemb for the output I had to use the On-Chip Ram library megawizard.
Now I want to check the resulting data in the On-Chip RAM, I want to see if it produced the right results. What should I do? What is the most straightforward easy way to do it?
I also meant to replace the readmemb with another On-Chip RAM and initialize with a .MIF. my intention is to create a synthetizable design not only simulated.
My board is the DE1-SOC, is there any application to easy get this information?
I'm looking into reading single bits from memory (RAM, harddisk). My understanding was, one can not read less than a byte.
However I read someone telling it can be done with assembly.
I wan't the bandwidth usage to be as low as possible and the to be retrieved data is not sequential, so I can not read a byte and convert it to 8 bits.
I don't think the CPU will read less than the size of a cache line from RAM (64 bytes on recent Intel chips). From disk, the minimum is typically 4 kiB.
Reading a single bit at a time is neither possible nor necessary, since the data bus is much wider than that.
You cannot read less than a byte from any PC or hard disk that I know of. Even if you could, it would be extremely inefficient.
Some machines do memory mapped port io that can read/write less than a byte to the port, but it still shows up when you get it as at least a byte.
Use the bitwise operators to pick off specific bits as in:
char someByte = 0x3D; // In binary, 111101
bool flag = someByte & 1; // Get the first bit, 1
flag = someByte & 2; // Get the second bit, 0
// And so on. The number after the & operator is a power of 2 if you want to isolate one bit.
// You can also pick off several bits like so:
int value = someByte & 3; // Assume the lower 2 bits are interesting for some reason
It used to be, say 386/486 days, where a memory was a bit wide, 1 meg by 1 bit, but you will have 8 or some multiple number of chips, one for each bit lane on the bus, and you could only read in widths of the bus. today the memories are a byte wide and you can only read in units of 32 or 64 or multiples of those. Even when you read a byte, most designs fill in the whole byte. it adds unnecessarily complication/cost, to isolate the bus all the way to the memory, a byte read looks to most of the system as a 32 or 64 bit read, as it approaches the edge of the processor (sometimes physical pins, sometimes the edge of the core inside the chip) is when the individual byte lane is separated out and the other bits are discarded. Having the cache on changes the smallest divisible read size from the memory, you will see a burst or block of reads.
it is possible to design a memory system that is 8 bits wide and read 8 bits at a time, but why would you? unless it is an 8 bit processor which you probably couldnt take advantage of a 8bit by 2 gig memory. dram is pretty slow anyway, something like 133 mhz (even your 1600mhz memory is only short burst as you read from slow parts, memory has not gotten faster in over 10 years).
Hard disks are similar but different, I think sectors are the smallest divisible unit, you have to read or write in those units. so when reading you have a memory cycle on the processor, no different that going to a memory, and depending on the controller either before you do the read or as a result, a sector is read of the disk, into a buffer, not unlike a cache line read, then your memory cycle to the buffer in the disk controller either causes a bus width read and the processor divides it up or if the bus adds complexity to isolate byte lanes then you isolate a byte, but nobody isolates bit lanes. (I say the word nobody and someone will come back with an exception...)
most of this is well documented, not hard to find. For arm platforms look for the amba and/or axi specifications, freely downloaded. the number of bridges, pcie controllers, disk controller documents are all available for PCs and other platforms. it still boils down to an address and data bus or one goesouta and one goesinta data bus and some control signals that indicate the access type. some busses have byte lane enables, which is generally for a write not a read. If I want to write only a byte to a dram in a modern 64 bit system, I DO have to tell everyone almost all the way out to the dram what I want to write. To write a byte on a memory module which must be accessed 64 bits at a time, at a minimum a 64 bit read happens into a temporary place either the cache or the memory controller, then the byte to be written modifies the specific byte within the 64 bit word, then that 64 bit quantity, eventually, is written back to the memory module itself. You can do this using a combination of the address bits and a few control signals or you can just put 8 byte lane enables and the lower address bits can be ignored. Hard disk, same deal, have to read a sector, modify one byte, then eventually write the whole sector at a time. with flash and eeprom, you can only write zeros (from the programmers perspective), you erase to ones (from the programmers perspective, is actually a zero in the logic, there is an inversion) and a write has to be a sector at a time, sectors can be 64 bytes, 128 bytes, 256 bytes typically.
I'm using a Tesla, and for the first time, I'm running low on CPU memory instead of GPU memory! Hence, I thought I could cut the size of my host memory by switching all integers to short (all my values are below 255).
However, I want my device memory to use integers, since the memory access is faster. So is there a way to copy my host memory (in short) to my device global memory (in int)? I guess this won't work:
short *buf_h = new short[100];
int *buf_d = NULL;
cudaMalloc((void **)&buf_d, 100*sizeof(int));
cudaMemcpy( buf_d, buf_h, 100*sizeof(short), cudaMemcpyHostToDevice );
Any ideas? Thanks!
There isn't really a way to do what you are asking directly. The CUDA API doesn't support "smart copying" with padding or alignment, or "deep copying" of nested pointers, or anything like that. Memory transfers require linear host and device memory, and alignment must be the same between source and destination memory.
Having said that, one approach to circumvent this restriction would be to copy the host short data to an allocation of short2 on the device. Your device code can retrieve a short2 containing two packed shorts, extract the value it needs and then cast the value to int. This will give the code 32 bit memory transactions per thread, allowing for memory coalescing, and (if you are using Fermi GPUs) good L1 cache hit rates, because adjacent threads within a block would be reading the same 32 bit word. On non Fermi GPUs, you could probably use a shared memory scheme to efficiently retrieve all the values for a block using coalesced reads.
Admittedly I don't get it. Say you have a memory with a memory word of length of 1 byte. Why can't you access a 4 byte long variable in a single memory access on an unaligned address(i.e. not divisible by 4), as it's the case with aligned addresses?
The memory subsystem on a modern processor is restricted to accessing memory at the granularity and alignment of its word size; this is the case for a number of reasons.
Speed
Modern processors have multiple levels of cache memory that data must be pulled through; supporting single-byte reads would make the memory subsystem throughput tightly bound to the execution unit throughput (aka cpu-bound); this is all reminiscent of how PIO mode was surpassed by DMA for many of the same reasons in hard drives.
The CPU always reads at its word size (4 bytes on a 32-bit processor), so when you do an unaligned address access — on a processor that supports it — the processor is going to read multiple words. The CPU will read each word of memory that your requested address straddles. This causes an amplification of up to 2X the number of memory transactions required to access the requested data.
Because of this, it can very easily be slower to read two bytes than four. For example, say you have a struct in memory that looks like this:
struct mystruct {
char c; // one byte
int i; // four bytes
short s; // two bytes
}
On a 32-bit processor it would most likely be aligned like shown here:
The processor can read each of these members in one transaction.
Say you had a packed version of the struct, maybe from the network where it was packed for transmission efficiency; it might look something like this:
Reading the first byte is going to be the same.
When you ask the processor to give you 16 bits from 0x0005 it will have to read a word from 0x0004 and shift left 1 byte to place it in a 16-bit register; some extra work, but most can handle that in one cycle.
When you ask for 32 bits from 0x0001 you'll get a 2X amplification. The processor will read from 0x0000 into the result register and shift left 1 byte, then read again from 0x0004 into a temporary register, shift right 3 bytes, then OR it with the result register.
Range
For any given address space, if the architecture can assume that the 2 LSBs are always 0 (e.g., 32-bit machines) then it can access 4 times more memory (the 2 saved bits can represent 4 distinct states), or the same amount of memory with 2 bits for something like flags. Taking the 2 LSBs off of an address would give you a 4-byte alignment; also referred to as a stride of 4 bytes. Each time an address is incremented it is effectively incrementing bit 2, not bit 0, i.e., the last 2 bits will always continue to be 00.
This can even affect the physical design of the system. If the address bus needs 2 fewer bits, there can be 2 fewer pins on the CPU, and 2 fewer traces on the circuit board.
Atomicity
The CPU can operate on an aligned word of memory atomically, meaning that no other instruction can interrupt that operation. This is critical to the correct operation of many lock-free data structures and other concurrency paradigms.
Conclusion
The memory system of a processor is quite a bit more complex and involved than described here; a discussion on how an x86 processor actually addresses memory can help (many processors work similarly).
There are many more benefits to adhering to memory alignment that you can read at this IBM article.
A computer's primary use is to transform data. Modern memory architectures and technologies have been optimized over decades to facilitate getting more data, in, out, and between more and faster execution units–in a highly reliable way.
Bonus: Caches
Another alignment-for-performance that I alluded to previously is alignment on cache lines which are (for example, on some CPUs) 64B.
For more info on how much performance can be gained by leveraging caches, take a look at Gallery of Processor Cache Effects; from this question on cache-line sizes
Understanding of cache lines can be important for certain types of program optimizations. For example, the alignment of data may determine whether an operation touches one or two cache lines. As we saw in the example above, this can easily mean that in the misaligned case, the operation will be twice slower.
It's a limitation of many underlying processors. It can usually be worked around by doing 4 inefficient single byte fetches rather than one efficient word fetch, but many language specifiers decided it would be easier just to outlaw them and force everything to be aligned.
There is much more information in this link that the OP discovered.
you can with some processors (the nehalem can do this), but previously all memory access was aligned on a 64-bit (or 32-bit) line, because the bus is 64 bits wide, you had to fetch 64 bit at a time, and it was significantly easier to fetch these in aligned 'chunks' of 64 bits.
So, if you wanted to get a single byte, you fetched the 64-bit chunk and then masked off the bits you didn't want. Easy and fast if your byte was at the right end, but if it was in the middle of that 64-bit chunk, you'd have to mask off the unwanted bits and then shift the data over to the right place. Worse, if you wanted a 2 byte variable, but that was split across 2 chunks, then that required double the required memory accesses.
So, as everyone thinks memory is cheap, they just made the compiler align the data on the processor's chunk sizes so your code runs faster and more efficiently at the cost of wasted memory.
Fundamentally, the reason is because the memory bus has some specific length that is much, much smaller than the memory size.
So, the CPU reads out of the on-chip L1 cache, which is often 32KB these days. But the memory bus that connects the L1 cache to the CPU will have the vastly smaller width of the cache line size. This will be on the order of 128 bits.
So:
262,144 bits - size of memory
128 bits - size of bus
Misaligned accesses will occasionally overlap two cache lines, and this will require an entirely new cache read in order to obtain the data. It might even miss all the way out to the DRAM.
Furthermore, some part of the CPU will have to stand on its head to put together a single object out of these two different cache lines which each have a piece of the data. On one line, it will be in the very high order bits, in the other, the very low order bits.
There will be dedicated hardware fully integrated into the pipeline that handles moving aligned objects onto the necessary bits of the CPU data bus, but such hardware may be lacking for misaligned objects, because it probably makes more sense to use those transistors for speeding up correctly optimized programs.
In any case, the second memory read that is sometimes necessary would slow down the pipeline no matter how much special-purpose hardware was (hypothetically and foolishly) dedicated to patching up misaligned memory operations.
#joshperry has given an excellent answer to this question. In addition to his answer, I have some numbers that show graphically the effects which were described, especially the 2X amplification. Here's a link to a Google spreadsheet showing what the effect of different word alignments look like.
In addition here's a link to a Github gist with the code for the test.
The test code is adapted from the article written by Jonathan Rentzsch which #joshperry referenced. The tests were run on a Macbook Pro with a quad-core 2.8 GHz Intel Core i7 64-bit processor and 16GB of RAM.
If you have a 32bit data bus, the address bus address lines connected to the memory will start from A2, so only 32bit aligned addresses can be accessed in a single bus cycle.
So if a word spans an address alignment boundary - i.e. A0 for 16/32 bit data or A1 for 32 bit data are not zero, two bus cycles are required to obtain the data.
Some architectures/instruction sets do not support unaligned access and will generate an exception on such attempts, so compiler generated unaligned access code requires not just additional bus cycles, but additional instructions, making it even less efficient.
If a system with byte-addressable memory has a 32-bit-wide memory bus, that means there are effectively four byte-wide memory systems which are all wired to read or write the same address. An aligned 32-bit read will require information stored in the same address in all four memory systems, so all systems can supply data simultaneously. An unaligned 32-bit read would require some memory systems to return data from one address, and some to return data from the next higher address. Although there are some memory systems that are optimized to be able to fulfill such requests (in addition to their address, they effectively have a "plus one" signal which causes them to use an address one higher than specified) such a feature adds considerable cost and complexity to a memory system; most commodity memory systems simply cannot return portions of different 32-bit words at the same time.
On PowerPC you can load an integer from an odd address with no problems.
Sparc and I86 and (I think) Itatnium raise hardware exceptions when you try this.
One 32 bit load vs four 8 bit loads isnt going to make a lot of difference on most modern processors. Whether the data is already in cache or not will have a far greater effect.