Atanasoff–Berry computer, the first computer with parallel processing[1]
Atanasoff–Berry computer, the first computer with parallel processing[1]

Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically ILP refers to the average number of instructions run per step of this parallel execution.[2]: 5 

Discussion

ILP must not be confused with concurrency. In ILP there is a single specific thread of execution of a process. On the other hand, concurrency involves the assignment of multiple threads to a CPU's core in a strict alternation, or in true parallelism if there are enough CPU cores, ideally one core for each runnable thread.

There are two approaches to instruction-level parallelism: hardware and software.

Hardware level works upon dynamic parallelism, whereas the software level works on static parallelism. Dynamic parallelism means the processor decides at run time which instructions to execute in parallel, whereas static parallelism means the compiler decides which instructions to execute in parallel.[3][clarification needed] The Pentium processor works on the dynamic sequence of parallel execution, but the Itanium processor works on the static level parallelism.

Consider the following program:

e = a + b
f = c + d
m = e * f

Operation 3 depends on the results of operations 1 and 2, so it cannot be calculated until both of them are completed. However, operations 1 and 2 do not depend on any other operation, so they can be calculated simultaneously. If we assume that each operation can be completed in one unit of time then these three instructions can be completed in a total of two units of time, giving an ILP of 3/2.

A goal of compiler and processor designers is to identify and take advantage of as much ILP as possible. Ordinary programs are typically written under a sequential execution model where instructions execute one after the other and in the order specified by the programmer. ILP allows the compiler and the processor to overlap the execution of multiple instructions or even to change the order in which instructions are executed.

How much ILP exists in programs is very application specific. In certain fields, such as graphics and scientific computing the amount can be very large. However, workloads such as cryptography may exhibit much less parallelism.

Micro-architectural techniques that are used to exploit ILP include:

It is known that the ILP is exploited by both the compiler and hardware support but the compiler also provides inherent and implicit ILP in programs to hardware by compile-time optimizations. Some optimization techniques for extracting available ILP in programs would include scheduling, register allocation/renaming, and memory access optimization.

Dataflow architectures are another class of architectures where ILP is explicitly specified, for a recent example see the TRIPS architecture.

In recent years, ILP techniques have been used to provide performance improvements in spite of the growing disparity between processor operating frequencies and memory access times (early ILP designs such as the IBM System/360 Model 91 used ILP techniques to overcome the limitations imposed by a relatively small register file). Presently, a cache miss penalty to main memory costs several hundreds of CPU cycles. While in principle it is possible to use ILP to tolerate even such memory latencies, the associated resource and power dissipation costs are disproportionate. Moreover, the complexity and often the latency of the underlying hardware structures results in reduced operating frequency further reducing any benefits. Hence, the aforementioned techniques prove inadequate to keep the CPU from stalling for the off-chip data. Instead, the industry is heading towards exploiting higher levels of parallelism that can be exploited through techniques such as multiprocessing and multithreading.[4]

See also

References

  1. ^ "The History of Computing". mason.gmu.edu. Retrieved 2019-03-24.
  2. ^ Goossens, Bernard; Langlois, Philippe; Parello, David; Petit, Eric (2012). "PerPI: A Tool to Measure Instruction Level Parallelism". Applied Parallel and Scientific Computing. 7133: 270–281. doi:10.1007/978-3-642-28151-8_27.
  3. ^ Hennessy, John L.; Patterson, David A. Computer Architecture: A Quantitative Approach.
  4. ^ Reflections of the Memory Wall

Further reading