Software prefetching compiler

On the other hand, when d is too big, it may not remain in the cache, but replaced with other memory reference. To reduce the number of instructions to be prefetched, we test prefetching placement for all memory instruction of a loop at the same time and remove redundancy. Redundancy is evaluated based on group reuse information.

We call an array access instruction redundant when they share a group reuse, that is, an array location referenced from one instruction may be referenced by other We only prefetch once when two array access instruction. We reduce the number of redundant prefetch insertion by analyzing group reuse. Advanced prefetching using a spatial locality: We further reduce the number of redundant prefetching by analyzing spatial reuse. When an array access has spatial reuse, its neighboring memory reference is going to be referenced in the near future and make it redundant to prefetch every iteration.

We test spatial locality using a simple test ; for each array access, if the last index is a function of induction variable of the innermost loop and truncated matrix except for the last index is loop-invariant , we consider this access having spatial locality. When we find the spatial locality in the array access, we unroll the innermost loop by the cache line size and then insert prefetch. One additional application of prefetching that we implemented was less focused on specific high-value opportunities.

This pass considered all loads from memory within a program and created a prefetch for them if the address being loaded was computed far enough away from the load site that a prefetch was likely to have a significant effect on latency. Rearranging the address computation code here was not necessary as it had been with recursive data structure prefetching. To test the results of our optimizations, we used both qualitative and quantitative methods.

During development, we tested our code by inspecting the llvm byte code files to see where prefetches were being inserted and compare these to the optimal locations that a human would choose. This let us check that our optimizations we behaving correctly and selecting good positions for prefetches, which after debugging, of course was found to be true. The next step was then to take quantitative measurements of program performance with or without our optimizations to assess the real impact that those prefetches were having on programs.

To do this, we compiled our optimized LLVM byte code to x assembly and then analyzed its performance using PTLsim, a cycle-accurate x86 microprocessor simulator. These were linked and simulated on the cluster machines using the default settings for PTLsim, and then we inspected statistics on data cache performance total cycles. We wrote several tests which contained targets for our optimizations and compared their performance with or without prefetching.

For recursive traversal prefetching on linked lists, we saw significant improvements due the addition of prefetches, with the total time going from 8,, cycles to 5,, cycles.

We suspect this may be a result of the hardware trying to prefetch the data but not having enough foresight to get it early enough. We suspect that even greater benefits from prefetching are theoretically possible in this case, because we discovered that the x prefetch instruction is only able to pull data into the L2 cache rather than all the way into the L1 cache.

When testing traversal prefetching for linked lists that used traversal loops rather than recursive calls, we got similar data. Total time went from 5,, cycles to 3,, cycles for the same underlying reasons. In testing trees we still saw some improvement in the cache behavior but it was less significant than expected, with some replays persisting.

Abstract - Cited by 70 21 self - Add to MetaCart Despite the success of instruction-level parallelism ILP optimizations in increasing the performance of microprocessors, certain codes remain elusive. In particular, codes containing recursive data structure RDS traversal loops have been largely immune to ILP optimizations, due to the fundamental serialization and variable latency of the loop-carried dependence through a pointer-chasing load.

To address these and other situations, we introduce decoupled software pipelining DSWP , a technique that statically splits a single-threaded sequential loop into multiple non-speculative threads, each of which performs useful computation essential for overall program correctness.

The resulting threads execute on thread-parallel architectures such as simultaneous multithreaded SMT cores or chip multiprocessors CMP , expose additional instruction level parallelism, and tolerate latency better than the original single-threaded RDS loop. To reduce overhead, these threads communicate using a synchronization array, a dedicated hardware structure for pipelined inter-thread communication. McKinley, Steven K.

Reinhardt, Charles C. Despite large caches, main-memory access latencies still cause significant performance losses in many applications. Numerous hardware and software prefetching schemes have been proposed to tolerate these latencies.

Software prefetching typically provides better prefetch accuracy than hardware, but i Abstract - Cited by 65 9 self - Add to MetaCart Despite large caches, main-memory access latencies still cause significant performance losses in many applications. Hardware prefetching can be effective at hiding these large latencies, but generates many useless prefetches and consumes considerable memory bandwidth. In this paper, we propose a cooperative hardware-software prefetching scheme called Guided Region Prefetching GRP , which uses compiler-generated hints encoded in load instructions to regulate an aggressive hardware prefetching engine.

Push vs. Lebeck - In International Conference on Supercomputing , As the performance gap between the CPU and main memory continues to grow, techniques to hide memory latency are essential to deliver a high performance computer system. Prefetching can often overlap memory latency with computation for array-based numeric applications. However, prefetching for pointe Abstract - Cited by 46 2 self - Add to MetaCart As the performance gap between the CPU and main memory continues to grow, techniques to hide memory latency are essential to deliver a high performance computer system.

We achieve this by using software prefetching instructions inserted automatically at compile-time. Through these prefetch instructions, we intend to bring the necessary data for each transaction from the main memory to the cache before the transaction itself starts to execute, thus converting the otherwise long latency cache misses into hits during the execution of the transaction.

We provide insights into when our technique is beneficial given certain characteristics of the transactional regions, the advantages and disadvantages of our approach, and finally, discuss potential solutions to overcome some of its limitations. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. If a function is called in two different transactions, we create one AP version for each call context. AP versions are transaction specific because the selection of the instructions for each AP depends on how the memory updates performed within the function affect its callers.

Comput Arch News 39 2 :1—7. Article Google Scholar. Dash A, Demsky B Automatically generating symbolic prefetches for distributed transactional memories.

In: Middleware Lecture Notes in Computer Science, vol Dash A, Demsky B Integrating caching and prefetching mechanisms in a distributed transactional memory. Diegues N, Romano P Time-warp: lightweight abort minimization in transactional memory. On some Intel hardware, it might only be 10 memory loads in flight at the same time.

This is generally a limited ressource, so, depending on the processor, you might want to organize your code so that you can always have 10 or whatever the number might be memory loads in flight at all time. Sometimes, it can be difficult to reorganize the code to keep a high number of instructions in flight, so software prefetches can help, but one should first determine that it is not possible to rewrite the code to take into account memory-level parallelism.

My source code is available. View all posts by Daniel Lemire. Stockfish is a good example. It even uses some extra code to pre-generate the hashkey of the next move before it is made to calculate the prefetch-address. Interesting topic. This is a topic that I would like to better understand, as this can influence how we try to optimise cache usage, as cache appears to be important for effective use of vector instructions. Does L1 cache store data or memory pages ie block of memory, say 64kb?

A similar question for L2 and L3 cache. The other unknown is for multi-thread coding, how is data shared between processor caches, especially if this data is changing.

I would very much appreciate if you can explain this relationship. On current Intel processors, I think L3 is always shared between the cores. L1 and L2 are on a per core basis. This will definitively be different in other types of processors. I find this a very interesting topic. Where does a prefetch instruction fit into this mix? When optimising data availability to vector registers, I expect this must depend on how data is moved through memory and the 3 levels of cache, in both directions.

I have achieved huge variations in performance of vector instructions, often without a clear understanding as to why. Hello, Daniel. I disagree with the points in this article so I made an example with regular loop that becomes faster with a prefetch. The answer is long so I made a post in my blog. The performance improvement is 1. We can say this is not a very good result and we will be right.

The main problem is that prefetch itself takes time for execution. I made an example where we can speedup summing one by one with prefetch. And the other point that regular memory access is not good a example for prefetch. Later I will write about cases where it is really good and can not be improved by code refactoring. But how would the compiler know a that the access pattern causes the auto prefech to be less effective, and b that the memory accessed is large enough to warrant the use of the prefech instruction?

Not to mention c that batch access with prefetch is more effective… would you imagine the compiler reorganizing non-batched code into batched code? So this criticism seems somewhat unfair.

The test code is designed to show the effects of randomly accessing non-cached RAM using different batch sizes. Consider the fact that the same test code works nearly 14 times faster if operating on already cached RAM.

I sent you an email privately just now. I should have emailed you privately to discuss my upcoming blog post, I did not. For that, I apologize. The source code I link to refers to your original gist, but my code is substantially different: I kept the main idea, but I simplified the code for the purpose of producing a simple example.