What is Thread Level Parallelism (TLP)? (Explained)

What is Thread Level Parallelism (TLP)?

Thread Level Parallelism, or TLP refers to the capability of the software to handle web applications, databases, and other high-end programs.

From the technical point of view, to produce the results, this technique uses several threads of the CPU at the same time.


  • Programs can do and deliver a lot more when they use Thread Level Parallelism even under heavy workloads.
  • Thread Level Parallelism is the technique to handle intensive programs.
  • Initially, this technique was used in the commercial servers but now it is used in most processors.
  • TLP is more logical than Instruction Level Parallelism since in this process separate threads are used for execution.
  • This type of parallelism is much more cost effective and can be exploited by following different strategies such as different variants of multithreading and Chip Multiprocessors.

Understanding Thread Level Parallelism (TLP)

What is Thread Level Parallelism (TLP)

Thread Level Parallelism is a process that helps in handling more tasks, data, and high-end programs by using more threads and executing the instructions in parallel.

Initially, TLP was used mainly by the commercial servers, but later on it also proved useful to the masses when processes and applications became more intensive and demanding.

The technology is now used more widely in the form of multi-core processors, which you will find in almost every desktop computer today.

Ideally, the need for TLP was felt due to the limitations observed in ILP or Instruction Level Parallelism, in spite of all the software and hardware techniques used to exploit it.

Ideally, the extent to which ILP can be exploited is limited to the following:

  • The type of hardware used
  • The number of virtual registers
  • The imperfect branch and jump predictors
  • Incorrect or ambiguous memory address

With all these discrepancies, a processor or the processing of data is sure to be far from ideal. The processor is sure to experience the following:

  • Data dependencies
  • Less or no control
  • Write After Read or WAR hazard, which is anti-dependency
  • Write After Write or WAW hazard, which is an output dependency
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Add to that, the issue is even exacerbated further due to fewer instructions per clock which affects memory accesses per cycle.

All these will result in higher complexity in using the capabilities of ILP which will mean the following:

  • Sacrificing maximum clock rate
  • Increased gap between sustained performance and leak issues rate
  • Increased energy usage per unit of performance

This means that ILP cannot be used for all types of applications, and this calls for other types of parallelism, such as Thread Level Parallelism or TLP.


The Thread Level parallelism mechanism has significant features that make it more useful. Some of the notable features of TLP are:

  • It is more logically structured, which is because separate threads are used for carrying out the instructions
  • Threads may follow a separate process which can either be a part of a parallel program of multiple processes or a separate program on its own

Apart from that, each of the threads may also have all the states such as:

All these are necessary for proper and faster execution of each process.

Thread Level Parallelism, much unlike ILP, does not exploit implicit parallel operations neither in a straight line nor in a loop code segment.

Instead, TLP is represented explicitly and uses several threads that are inherently parallel for execution.

TLP is more cost-effective to exploit, which makes it a good alternative to ILP. It occurs naturally in several server applications, as it does in other vital applications.

When TLP is exploited in the right way, the functional units are kept busy to enhance the overall performance of the system by reducing dependencies and stalls.

Ideally, it is best done by combining both ILP and TLP together.

What are the Different Types of Thread Level Parallelism?

Typically, the types of Thread Level Parallelism depend on the strategies followed in order to exploit parallelism.

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Based on this aspect, TLP can be of two specific types such as multithreading, along with its variants, and Chip Multi-Processors or CMPs.


This process, just as the name suggests, uses several threads to share the functional units available in a CPU in an overlapping manner.

The independent state of every thread is duplicated by the processor in order to enable this process, which has everything separate such as:

  • A separate program counter
  • A separate register file
  • A separate page table

The memory is also shared by using virtual memory mechanisms, which support multiprogramming.

Multithreading is done in two ways as follows:


In fine-grained multithreading, the execution of different threads is interleaved by switching on every instruction.

It is usually done in a round-robin manner and the stalled threads are skipped at the time of switching. The CPU can switch threads on each clock cycle.

The major advantage of this process is that the losses in output due to long and short stalls can be hidden because instructions are executed from other threads when one is stalled.

However, the process slows down the implementation of the individual threads.


In coarse-grained multithreading, thread switches and instructions will be issued from other threads only when there is an expensive stall, a Level 2 cache miss, for instance.

This gives some time to switch and therefore will not slow down the processor.

In this process, however, throughput losses cannot be overcome easily, especially from shorter stalls due to the pipeline startup costs.

This is because in this process the instructions are issued by the CPU during a stall to empty or freeze the pipeline and fill it with instructions from a new thread.

This overhead is useful to reduce the penalty of expensive stalls because the refill of the pipeline is insignificant in comparison to the stall time.

A few other variants of multithreading are used in TLP, which also determine its types. These are:

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Simultaneous Multithreading or SMT

In this specific type of multithreading technique, there are several threads utilized for execution.

All these threads share the same CPU to reduce underutilization of resources by making more efficient allocation of the same.

The distinctive features of this specific process are:

  • A pool of execution units
  • Multiple logical processors with a copy of state for each of them
  • Several threads running concurrently
  • Better utilization of resources
  • Latency tolerance

Chip Multi-Processors or CMP

In this particular technique, every single thread operates independently in order to execute on its own using its mini processor. This causes less interference between the threads and also results in a simple design.

The distinctive features of this specific process are:

  • Much simpler cores with a reasonable amount of parallelism
  • Concurrently running threads on different cores
  • Integrated multiple processors with chip multiprocessors on a single chip
  • Ease in interconnecting and packing multiple processors that lowers latency, off-chip signaling
  • Better communication and synchronization between processors
  • Reasonably short cycle time
  • Reduced hardware overhead
  • Reduced power consumption

However, this process may result in wastage of resources while running multithreaded applications or multi-programmed workloads if the application cannot be decomposed effectively into threads.


Thread Level Parallelism is a very useful and effective alternative to Instruction Level Parallelism.

It can handle large datasets in parallel and keep the functional units busy as well.

It allows for a better overall performance. It also helps in better allocation of the obtainable resources of the CPU, thus lowering overheads.

About Taylor Swift

Taylor SwiftTaylor Swift, a UOPEOPLE graduate is a freelance technology writer with in-depth knowledge about computers. She has an understanding of hardware and technology gained through over 10 years of experience. Follow Her at Linkedin

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